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Decision making theories: Implications for Academic Advising, by Tina Brazil and Jim Levin

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Dr Ghaiath Hussein
Dr Ghaiath Hussein

The document discusses various decision making theories that have implications for academic advising. It defines rational decision making and lists criteria for rational decisions. It also discusses problems with qualitative decision making methods like heuristics and biases. Several decision making models are presented: Goals, Options and Outcomes model; Personalist Approach; Lens Model; Simple Utility Equation; and Additive Linear Multi-Attribute Utility Theory. This last model uses quantitative attributes to assign values and probabilities to options to determine their overall utility. The document provides an example of how this model was used to predict student graduation probabilities.

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DECISION MAKING THEORY Implications for Academic Advising Tina Brazil (TAB291@gmail.com) Jim Levin (JL7@psu.edu)
Importance: Curriculum of Academic Advising ,[object Object],[object Object]
Definition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Problems with Qualitative Decision Making Methods ,[object Object],[object Object],[object Object],[object Object]
Models ,[object Object],[object Object],[object Object],[object Object],[object Object]
Definition of “Utility” ,[object Object],[object Object],[object Object]
Goals, Options, and Outcomes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Personalist Approach (first approximation of quantifiable decision making Fig 1. The pluses and minuses are assigned on an arbitrary scale decided by the decision maker. (Brown, 2005) + + + + + + + Total (algebraic) - - + + Career security + + + + + Academic success + + + + - - Class enjoyment Liberal Arts Science OUTCOMES OPTIONS
Lens Model Concept ,[object Object],[object Object],[object Object],[object Object]
Simple Utility Equation ,[object Object],[object Object]
Simple Utility Equation study play pass .2 fail .8 pass .7 fail .3 Value prob x value utility -100 -.3 .4 +100 .7 -100 -.8 -.6 +100 .2 Figure 2. Here, the two options are study and play. In this case, the utility of option “study” has a much higher utility value to the decision maker than does play. Utility = Σ (probability outcome x value outcome )
Additive Linear Multi-Attribute Utility Theory (MAUT) ,[object Object]
Example of MAUT ,[object Object],[object Object]
Additive Linear Multi-Attribute Utility Theory (MAUT): ENGINEERING ,[object Object],[object Object],[object Object]
MAUT Example: SC   PHYS_ATT -0.0776     0.1678 .1818 (.0429) MATH_ATT -0.0819     0.1555   CHEM_ATT (<0.0493) (<.0001) -0.0161   -0.0173 -.0136 (<.0001) NONSCIpts       -0176 (.0231) ENGL score -0.0013 (<.0001) 0.1282   0.0798 .0759 (<.0001) CHEM score -0.055   0.3735     M40_score         M140_score PHYSICAL MATH LIFE ALL  
MAUT Example cont: SC         BASIC MATH SCORE -0.0179 (<.0001) -1.3727   -1.1434 -1.2080 (<.0001) REMEDIAL_MATH         SAT_MATH -0.0565 0.0033     .0022 (.0111) SAT_VERB -0.0111 (<.0001)   2.1597 0.9413 .8618 (<.0001) HS_GPA         Gender         Study_psu -0.0469 0.0288     .0142 (.0138) Study_hs -0.0104   1.7525   .2727 (.0171) COMPLETELY CERTAIN       -.2241 (.0929) CERTAIN       .8433 (.0138) KNOW_SOME         KNOW_ALOT -0.0358   -0.7194     BIOL_ATT
References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Decision making theories: Implications for Academic Advising, by Tina Brazil and Jim Levin

  • 1. DECISION MAKING THEORY Implications for Academic Advising Tina Brazil (TAB291@gmail.com) Jim Levin (JL7@psu.edu)
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8. The Personalist Approach (first approximation of quantifiable decision making Fig 1. The pluses and minuses are assigned on an arbitrary scale decided by the decision maker. (Brown, 2005) + + + + + + + Total (algebraic) - - + + Career security + + + + + Academic success + + + + - - Class enjoyment Liberal Arts Science OUTCOMES OPTIONS
  • 9.
  • 10.
  • 11. Simple Utility Equation study play pass .2 fail .8 pass .7 fail .3 Value prob x value utility -100 -.3 .4 +100 .7 -100 -.8 -.6 +100 .2 Figure 2. Here, the two options are study and play. In this case, the utility of option “study” has a much higher utility value to the decision maker than does play. Utility = Σ (probability outcome x value outcome )
  • 12.
  • 13.
  • 14.
  • 15. MAUT Example: SC   PHYS_ATT -0.0776     0.1678 .1818 (.0429) MATH_ATT -0.0819     0.1555   CHEM_ATT (<0.0493) (<.0001) -0.0161   -0.0173 -.0136 (<.0001) NONSCIpts       -0176 (.0231) ENGL score -0.0013 (<.0001) 0.1282   0.0798 .0759 (<.0001) CHEM score -0.055   0.3735     M40_score         M140_score PHYSICAL MATH LIFE ALL  
  • 16. MAUT Example cont: SC         BASIC MATH SCORE -0.0179 (<.0001) -1.3727   -1.1434 -1.2080 (<.0001) REMEDIAL_MATH         SAT_MATH -0.0565 0.0033     .0022 (.0111) SAT_VERB -0.0111 (<.0001)   2.1597 0.9413 .8618 (<.0001) HS_GPA         Gender         Study_psu -0.0469 0.0288     .0142 (.0138) Study_hs -0.0104   1.7525   .2727 (.0171) COMPLETELY CERTAIN       -.2241 (.0929) CERTAIN       .8433 (.0138) KNOW_SOME         KNOW_ALOT -0.0358   -0.7194     BIOL_ATT
  • 17.