Understanding Mindshift Learning: The Transition to Object-Oriented Development - PowerPoint PPT Presentation

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Understanding Mindshift Learning: The Transition to Object-Oriented Development

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  1. Understanding Mindshift Learning: The Transition to Object-Oriented Development Deborah J. Armstrong and Bill C. Hardgrave MIS Quarterly (in press)

  2. Motivation • IT professionals are repeatedly asked to learn new tools, techniques and processes • Transitions often require a shift in mindset (mindshift) • Examples: • Shift from mainframe to client-server • Move to object-oriented software development • Without mindshift advantages may be lost • Why is learning during a mindshift so difficult?

  3. Context • Iivari, Hirschheim and Klein’s (1998; 2000-2001) Information Systems (IS) development framework • Four hierarchical levels of framework: • Paradigm • Approach • Methodology • Technique

  4. More Context • Differences in Traditional and OO development occur at the approach level. Iivari et al (2000-2001) • Approach components: • Set of goals • Guiding principles and beliefs • Fundamental concepts • Principles for the ISD process • Learning process may begin with individuals being introduced to the fundamental concepts of the new approach.

  5. Literature Review • Three themes in software development literature from learning / knowledge structures perspective: • Successful IS development education focused on semantics first, then syntax (e.g. Spohrer & Soloway, 1986; Hardgrave and Doke, 2000) • Experts create abstract (semantically focused) knowledge structures, novices have more concrete (syntactically focused) knowledge structures (e.g. Adelson, 1981; 1984) • Experienced developers have difficulty moving from traditional to OO approach (e.g. Rosson & Alpert, 1990)

  6. Learning Process • Knowledge Structure • A representation of an individual’s knowledge which includes domain-specific concepts and the relations among those concepts(Dorsey, Campbell, Foster & Miles, 1999) • Concept Knowledge • Ideas and information embodied in the knowledge(Ausubel, 1963) • Incremental learning • Mindshift learning • Proactive Interference(Underwood, 1957)

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

  8. Refining the Theory • Motivation: • Strengthen theory • Context specific • Identified OO concepts • Inductive Approach • Gathered insights from experts

  9. 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

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

  11. Hypotheses H1. A developer’s OO concept knowledge score will have a U-shaped (curvilinear) relationship with the degree of perceived novelty. H2. A developer’s carryover concept knowledge score will be greater than his or her changed concept knowledge score. H3. A developer’s carryover concept knowledge score will be greater than his or her novel concept knowledge score. H4. A developer’s novel concept knowledge score will be greater than his or her changed concept knowledge score.

  12. Method • Subjects • Sample criteria: both traditional and OO experience • 81 software developers (response rate 39%) • 16 organizations • Instrument Development • Degree of perceived novelty (9 items) • OO concept knowledge (27 items) • Level of perceived difficulty (9 items) • Validation

  13. Hypothesis Testing – H1 OO Concept Knowledge Score = α + β1*Novelty + β2*Novelty2

  14. Data Preparation • Categorize concepts (based on degree of perceived novelty) • Carryover = 0-24% • Changed = 25-75% • Novel = 76-100% • Placed scores for each concept into categories

  15. Object Concept Categorization

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

  17. Results • Why no significant difference between Novel and Carryover ? • Rival hypothesis analysis • Years of OO experience • category • concept • Difficulty

  18. Data Analysis Years of OO Experience

  19. Data AnalysisYears of OO Experience/Category

  20. Data AnalysisYears of OO Experience/Concept

  21. Data AnalysisLevel of Concept Difficulty

  22. Bottom Line It is not years of OO experience It is not difficulty of the OO concept It is the perceived novelty of the OO concept that impacts learning

  23. Implications • Individual • Increase productivity, increase employee satisfaction, increase employee retention • Organizational • Decrease training costs, increase software quality, encourage wider adoption of OO • Transitions • Training decision aid, change management initiatives, increase commitment

  24. Future Research • Goal • Understand IS professionals mental models and changes in mental models across transitions • Projects • Test full theory • Test various antecedent conditions (e.g., multiple existing mental models) • Test various contexts (e.g., ERP, SOA)