Qualitative motivation with sets and relations.

Area of Science:

• Psychology
• Educational Psychology
• Behavioral Science

Background:

• Self-determination theory (SDT) posits multiple, qualitatively distinct regulations of motivation.
• Existing approaches often use numerical methods, potentially oversimplifying complex motivational structures.
• Disputes exist regarding the unidimensionality versus multidimensionality of motivation within SDT.

Purpose of the Study:

• To introduce a discrete mathematical framework using sets and relations for modeling motivation in SDT.
• To explore combinatorial conceptualizations of motivation beyond traditional numerical approaches.
• To address the unidimensionality vs. multidimensionality debate in motivation research.

Main Methods:

• Utilized discrete mathematical models from the theory of knowledge spaces.
• Applied relational data mining, specifically inductive item tree analysis (IITA) on a learning motivation dataset.
• Employed Boolean analysis of questionnaires to extract motivation structures.

Main Results:

• Developed a versatile set-theoretic model for combinatorial motivation conceptualization.
• Identified approximately chain-like motivation orders with quantifiable combinatorial details.
• Discovered two reducible combinatorial dimensions, offering insights into motivation's structure and measurement.
• Enumerated potential subforms of motivation through empirical analysis of regulation types.

Conclusions:

• The set-theoretic approach provides a more nuanced understanding of motivational regulations than numerical methods.
• This framework can identify distinct, empirically separable motivational subtypes, resolving inconsistencies in SDT applications.
• The methodology offers potential for developing adaptive assessment and training procedures in SDT contexts.