Understanding Mindshift Learning: The Transition to Object-Oriented Development .

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Understanding Mindshift Learning: The Transition to Object-Oriented Development. Deborah J. Armstrong and Bill C. Hardgrave MIS Quarterly (in press). Motivation. IT professionals are repeatedly asked to learn new tools, techniques and processes
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Understanding Mindshift Learning: The Transition to Object-Oriented Development Deborah J. Armstrong and Bill C. Hardgrave MIS Quarterly (in press)

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Motivation IT experts are more than once requested that learn new devices, strategies and procedures Transitions regularly require a move in mentality (mindshift) Examples: Shift from centralized computer to customer server Move to protest arranged programming improvement Without mindshift focal points might be lost Why is learning amid a mindshift so troublesome?

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Context Iivari, Hirschheim and Klein\'s (1998; 2000-2001) Information Systems (IS) advancement structure Four various leveled levels of structure: Paradigm Approach Methodology Technique

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More Context Differences in Traditional and OO improvement happen at the approach level. Iivari et al (2000-2001) Approach segments: Set of objectives Guiding standards and convictions Fundamental ideas Principles for the ISD procedure Learning procedure may start with people being acquainted with the central ideas of the new approach.

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Literature Review Three topics in programming improvement writing from learning/information structures point of view: Successful IS advancement training centered around semantics initially, then language structure (e.g. Spohrer & Soloway, 1986; Hardgrave and Doke, 2000) Experts make conceptual (semantically engaged) learning structures, fledglings have more concrete (grammatically engaged) information structures (e.g. Adelson, 1981; 1984) Experienced designers experience issues moving from customary to OO approach (e.g. Rosson & Alpert, 1990)

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Learning Process Knowledge Structure A representation of an individual\'s learning which incorporates space particular ideas and the relations among those ideas (Dorsey, Campbell, Foster & Miles, 1999) Concept Knowledge Ideas and data encapsulated in the information (Ausubel, 1963) Incremental learning Mindshift learning Proactive Interference (Underwood, 1957)

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Base Theory Existing Knowledge Structure Modification (incremental) Introduce Concepts Concept Knowledge New Knowledge Structure Creation (mindshift)

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Refining the Theory Motivation : Strengthen hypothesis Context particular Identified OO ideas Inductive Approach Gathered experiences from specialists

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OO Software Development Knowledge Structure Revised Theory Traditional Software Development Knowledge Structure OO Software Development Concept Knowledge OO Software Development Concepts Learning Novel Changed Carryover

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Hypotheses Development High OO Concept Knowledge Score Low High Degree of Novelty

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Hypotheses H1. An engineer\'s OO idea learning score will have a U-formed (curvilinear) association with the level of saw curiosity. H2. An engineer\'s vestige idea learning score will be more prominent than his or her changed idea information score. H3. A designer\'s remainder idea information score will be more noteworthy than his or her novel idea learning score. H4. A designer\'s novel idea information score will be more noteworthy than his or her changed idea learning score.

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Method Subjects Sample criteria: both conventional and OO encounter 81 programming engineers (reaction rate 39%) 16 associations Instrument Development Degree of saw oddity (9 things) OO idea learning (27 things) Level of saw trouble (9 things) Validation

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Hypothesis Testing – H1 OO Concept Knowledge Score = α + β 1 * Novelty + β 2 * Novelty 2

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Data Preparation Categorize ideas (in light of level of saw oddity) Carryover = 0-24% Changed = 25-75% Novel = 76-100% Placed scores for every idea into classifications

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Object Concept Categorization

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Hypothesis Testing Category Means Novel = 2.29 Changed = 1.62 Carryover = 2.24 t Tests

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Results Why no huge distinction amongst Novel and Carryover ? Match speculation examination Years of OO experience classification idea Difficulty

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Data Analysis Years of OO Experience

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Data Analysis Years of OO Experience/Category

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Data Analysis Years of OO Experience/Concept

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Data Analysis Level of Concept Difficulty

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Bottom Line It is not years of OO experience It is not trouble of the OO idea It is the apparent curiosity of the OO idea that effects learning

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Implications Individual Increase efficiency, increment worker fulfillment, increment representative maintenance Organizational Decrease preparing costs, increment programming quality, empower more extensive reception of OO Transitions Training choice guide, change administration activities, increment responsibility

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Future Research Goal Understand IS experts mental models and changes in mental models crosswise over moves Projects Test full hypothesis Test different precursor conditions (e.g., numerous current mental models) Test different settings (e.g., ERP, SOA)

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