Your SlideShare is downloading. ×
Decision making theories: Implications for Academic Advising, by Tina Brazil and Jim Levin
Upcoming SlideShare
Loading in...5

Thanks for flagging this SlideShare!

Oops! An error has occurred.

Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

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


Published on

Published in: Education, Technology

  • Be the first to comment

No Downloads
Total Views
On Slideshare
From Embeds
Number of Embeds
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

No notes for slide


  • 1. DECISION MAKING THEORY Implications for Academic Advising Tina Brazil ( Jim Levin (
  • 2. Importance: Curriculum of Academic Advising
    • “Academic Advising …….. This curriculum includes …….. decision-making ……..” (NACADA, 2006).
    • “ … use complex information …. reach decisions … “ (NACADA, 2006).
  • 3. Definition
    • Rational decisions are ones that advances the welfare of the decision maker effectively and logically based on everything the decision maker knows and feels (Brown, 2005, p. 54)
    • Criteria for rational decisions (Hastie & Dawes, 2001, p.18)
      • It is based on the decision maker’s current assets. Assets include not only money, but physiological state, psychological capacities, social relationships, and feelings.
      • It is based on the possible consequences of the choice.
      • When these consequences are uncertain, their likelihood is evaluated according to the basic rules of probability theory.
      • It is a choice that is adaptive within the constraints of those probabilities and the values or satisfactions associated with each of the possible consequences of choice.
  • 4. Problems with Qualitative Decision Making Methods
    • Heuristics: speculative formulation serving as a guide in the investigation or solution of a problem (Keren and Teigen, 2004)
      • Representation bias: decision making by recalling a memory or experience that is similar to the present decision making experience
      • Availability bias: decision making by the primacy and/or by predicting “easily conceivable outcomes”
      • Anchor & adjustment bias: decision making by what is familiar and conceivable
  • 5. Models
    • Goals, Options and Outcomes (GOO)
    • The Personalist Approach
    • Lens Model
    • Simple Utility Equation
    • Additive Linear Multi-Attribute Utility Theory (MAUT)
  • 6. Definition of “Utility”
    • “ The consumption utility of an option is broadly defined here as the benefit the option delivers.” (Hsee, 1999, p. 555)
    • Furthermore, it is assumed that the decision maker should choose the option that delivers the greatest utility or benefit. (Hsee, 1999)
    • When making decisions, we think about what option will derive the highest utility. (Hastie and Dawes, 2001, p. 200)
  • 7. Goals, Options, and Outcomes
    • What do I want? (goals)
    • What can I do? (options)
    • What might happen? (outcomes) (Brown, 2005, p. 9)
    • In Practice
      • Goals & options can be listed
      • Difficult to predict outcomes
        • Quantitative methods can be used (simple probability theory)
          • Research indicates that quantitative methods predict outcomes better than qualitative methods (Hastie & Dawes, 2001)
  • 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. Lens Model Concept
    • The decision maker is trying to see a “distal” true state of something through a “proximal lens” of cues. These cues represent information or characteristics that the decision maker uses to make a decision (Hastie and Dawes, 2001)
    • An algebraic model of probability that measures and assigns a scaled weight to the importance of each piece of information (cue) available to the decision maker (Hastie and Dawes, 2001)
    • Research: “experts correctly select variables that are important in making predictions, but that a probability model combines these variables in a way that is superior to the global judgments of these very same experts.” (Hastie and Dawes, 2004, p. 58)
    • Probability models have been derived from the Lens Model Concept.
  • 10. Simple Utility Equation
    • Decision tree in which each option represents a major branch, and from each branch stems the possible outcomes. Each of these outcomes is assigned a specific quantitative probability so that the sum of the outcomes stemming from one choice adds up to 1, or 100%. The probability for each outcome is multiplied by an assigned number that represents how the decision maker would feel about that outcome (Hastie and Dawes, 2001)
    • Utility = Σ (probability outcome x value outcome )
  • 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. Additive Linear Multi-Attribute Utility Theory (MAUT)
    • MAUT weighs all of the attributes and scales the attributes by importance to the decision maker. Each option is considered by assigning a scaled value to that option-attribute, according to its importance, and then adding up all of the scaled option-attributes to obtain the utility value for that option (Hastie and Dawes, 2001)
  • 13. Example of MAUT
    • To predict the probability of a student graduating from ENGR, Dr. James Levin constructed a probability model (logic regressions) that used several quantitative and qualitative (which were quantified) student criteria as inputs, assigned a scaled value to each criterion, and produced an output that gave the probability of that student graduating from ENGR. (Levin & Wyckoff, 1998)
    • Similar models are in progress for SC (Levin, 2007)
  • 14. Additive Linear Multi-Attribute Utility Theory (MAUT): ENGINEERING
    • Grad Engr/Not Grad Engr = -3.8 -.02 x 10 (nspts) -.01 x 520(satv) +.69 x 3.00(hsgpa) +.08 x 25(alg) +.07 x 12(chem-s) + .28 x 1(reas-g) +.20 x 1(gen) = .236
    • Odds of Grad Engr/Not Grad Engr = e .236 = 2.72 .236 = 1.27/1
    • Probability = 1.27/2.27 = 56%
  • 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. References
    • Brown, Rex, Rational Choice and Judgment: Decision Analysis for the Decider, John Wiley & Sons, Inc., Hoboken, New Jersey, 2005.
    • Hastie, Reid and Robyn M. Dawes, Rational Choice in an Uncertain World: The Psychology of Judgment and Decision Making, Sage Publications, Thousand Oaks, California, 2001.
    • Hsee, Christopher K. (1999). Value Seeking and Prediction-Decision Inconsistency: Why Don’t People Take What They Predict They’ll Like the Most? Psychonomic Bulletin & Review, 6 (4), 555-561.
    • Keren, Gideon and Karl H. Teigen, “Yet Another Look at the Heuristics and Biases Approach.” Blackwell Handbook of Judgment and Decision Making, Blackwell Publishing Ltd., Malden, MA, 2004.
    • Levin, James and Wykoff, Jack,(1998). Effective Advising: Identifying Students Most Likely to Persist and Succeed in Engineering. Engineering Education, 75(11), 178-182.
    • Levin, James, (2007). Effective Advising: Identifying Students Most Likely to Persist and Succeed in Science in progress.
    • NACADA: National Academic Advising Association. (2006). NACADA concept of academic advising. Retrieved 5/2/07 from