Novice level knowledge acquisition using a technology-based educational delivery system: The role of experiential practice

Increasing constraints on personnel and resources have led to a focus on alternative methods of transmitting knowledge to novices, whether university students or newly hired staff. This chapter focuses on one such alternative through the use of a technology-based educational delivery system (TBEDS). Prior research has addressed individual components of technology-supported systems or performed experiments of limited time and direct external relevance to the participants, but has not addressed the effect of a holistic approach to technology-based learning on users. This study capitalizes on a unique, holistically designed TBEDS to longitudinally examine the impact of systems on novices' procedural knowledge acquisition under conditions of actual usage. The longitudinal data also illustrates the role of user-determined experiential practice on achievement as moderated by comfort with technology. The findings indicate a strong relationship between the use of a TBEDS for repeated experiential practice and procedural knowledge acquisition. Individual components of effort (quantity of problems attempted, frequency of practice sessions, and quality of practice) are examined, with quantity being significantly positively related to performance, as is quality when the user is accountable to an external authority for that quality.

[1]  Steve G. Sutton,et al.  The Effect of Alternative Procedural Explanation Types on Procedural Knowledge Acquisition during Knowledge-Based Systems Use , 2007, J. Inf. Syst..

[2]  Dale Goodhue,et al.  Task-Technology Fit and Individual Performance , 1995, MIS Q..

[3]  Izak Benbasat,et al.  Explanations From Intelligent Systems: Theoretical Foundations and Implications for Practice , 1999, MIS Q..

[4]  Jane Fedorowicz,et al.  A Learning Curve Analysis of Expert System Use , 1992 .

[5]  John R. Anderson,et al.  Reflections of the Environment in Memory Form of the Memory Functions , 2022 .

[6]  M. Levine,et al.  Effects of Goal Specificity on Means-Ends Analysis and Learning. , 1982 .

[7]  C. J. Wolfe,et al.  The effects of system design alternatives on the acquisition of tax knowledge from a computerized tax decision aid , 2000 .

[8]  John Sweller,et al.  Cognitive Load During Problem Solving: Effects on Learning , 1988, Cogn. Sci..

[9]  I. Benbasat,et al.  The Influence of Decision Aids on Choice Strategies: An Experimental Analysis of the Role of Cognitive Effort , 1994 .

[10]  C. Cloyd,et al.  Performance in Tax Research Tasks: The Joint Effects of Knowledge and Accountability , 1998 .

[11]  John R. Anderson.  Acquisition of cognitive skill. , 1982 .

[12]  Steven Hornik,et al.  Expert Systems Usage and Knowledge Acquisition: An Empirical Assessment of Analogical Reasoning in the Evaluation of Internal Controls , 1998 .

[13]  J. Sweller,et al.  Development of expertise in mathematical problem solving. , 1983 .

[14]  J. Sweller,et al.  The Use of Worked Examples as a Substitute for Problem Solving in Learning Algebra , 1985 .

[15]  J. Hal Reneau,et al.  The Effects of Memory Structure on Using Rule‐Based Expert Systems for Training: A Framework and an Empirical Test* , 1990 .

[16]  John Sweller,et al.  Some cognitive processes and their consequences for the organisation and presentation of information , 1993 .

[17]  Richard M. Tubbs,et al.  The effect of experience on the auditor's organization and amount of knowledge , 1988 .

[18]  F. Paas,et al.  Cognitive Architecture and Instructional Design , 1998 .

[19]  P. Chandler,et al.  Evidence for Cognitive Load Theory , 1991 .

[20]  Robert Libby,et al.  Determinants of judgment performance in accounting settings: Ability, knowledge, motivation , 1993 .

[21]  Vicky Arnold,et al.  Explanation provision and use in an intelligent decision aid , 2004, Intell. Syst. Account. Finance Manag..

[22]  John R. Anderson,et al.  The role of examples and rules in the acquisition of a cognitive skill. , 1997, Journal of experimental psychology. Learning, memory, and cognition.

[23]  James E. Hunton,et al.  Incorporating information technology considerations into an expanded model of judgment and decision making in accounting , 2000, Int. J. Account. Inf. Syst..

[24]  John Sweller,et al.  Cognitive technology: Some procedures for facilitating learning and problem solving in mathematics and science. , 1989 .

[25]  Paul John Steinbart,et al.  The Effects of Explanation Type and User Involvement on Learning from and Satisfaction with Expert Systems , 1998 .

[26]  P. Chandler,et al.  Cognitive load as a factor in the structuring of technical material. , 1990 .

[27]  John R. Anderson,et al.  Skill Acquisition: Compilation of Weak-Method Problem Solutions. , 1987 .

[28]  J. Sweller,et al.  What do students learn while solving mathematics problems , 1985 .

[29]  Robert H. Ashton,et al.  Judgment and decision-making research in accounting and auditing: Acknowledgments , 1995 .

[30]  Constance M. Lehmann,et al.  The Effects of Experience on Complex Problem Representation and Judgment In Auditing: An Experimental Investigation , 2006 .

[31]  Steven M. Glover,et al.  The Influence of Decision Aids on User Behavior: Implications for Knowledge Acquisition and Inappropriate Reliance , 1997 .

[32]  Robert Libby,et al.  Judgment and decision-making research in accounting and auditing: The role of knowledge and memory in audit judgment , 1995 .

[33]  Graham Gal,et al.  Interface Style and Training Task Difficulty as Determinants of Effective Computer‐Assisted Knowledge Transfer* , 1992 .

[34]  John R. Anderson,et al.  Learning and Memory: An Integrated Approach , 1994 .

[35]  Steve G. Sutton,et al.  Explanation Provision in Knowledge‐Based Systems: A Theory‐Driven Approach for Knowledge Transfer Designs , 2004 .

[36]  Clark Hampton,et al.  Determinants of reliance: An empirical test of the theory of technology dominance , 2005, Int. J. Account. Inf. Syst..