Compurers Educ. Vol. 13. No. 2, pp. 147-155, 1989 Printed in Great Britain. All rights reserved

0360.1315i89 53.00 +o.oo Copyright c 1989 Pergamon Press plc

CAPTURING TEACHERS’ DECISION-MAKING POLICIES USING A MICROCOMPUTER SIMULATION

DEAN M. OISBOID, LEEANN ETTIPU’GER, JAMAL ABEDI and RICHARD J, SHAVELSGX Graduate School of Education, University of California, Santa Barbara. CA 93105, U.S.A.

(Received 4 August 1988)

Abstract-This paper describes a computer simulation for educating teachers in classroom planning and decision-making. The simulation was based on research by Borko and Cone. The former explored teachers’ preinstructionai decisions and classroom objectives; the latter focused on decisions about managing student behavior. In both studies, scenarios were used to describe student characteristics and behavior, and the teachers made pedagogical decisions about the students. The scenarios were based on factorial designs which systematically manipulated variables known to be important in teacher’s decisions. The simulation uses a fractional factorial of the full experimental design to reduce the number of scenarios the teacher needs to respond to in the simulation while permitting the simulation to estimate the teacher’s decision-making policy (details of fractional factorial designs are presented in the paper). The program then compares the teacher’s observed policy with the ideal decision model also obtained from the teacher. The teacher has the option to modify the ideal, the observed, or both policies and to re-run the simulation with a new set of scenarios. A concrete example is provided to illustrate the simulation and results.

INTRODUCTION

Microcomputer simulations have been used for teacher preparation in lesson planning[3J, special education[4J, and elementary school preparation[.5]. They are only beginning to enter the arena of teachers’ cIassroom decision-making, making their way from research.

Typically research into teachers’ decision-making processes has been to isolate variables that teachers consider important, such as student’s behavior and achievement, construct scenarios about students and classroom events based on those variables, and then have the teacher make decisions for each of the scenarios[1,2,6-8,131. These types of simulations tend to involve a large amount of paperwork both for the experimenter and the teachers.

For purposes of educating teachers in decision-making, a great amount of the papershuffling could be removed by adapting the simulation to a microcomputer. The software would replace the scenario cards and answer sheets. A unique advantage is that the microcomputer can be used for data collection and immediate data analysis. This allows for fast feedback to the teacher and experimenter.

One purpose of this study was to examine the efficacy of converting such a card-based simulation to a microcomputer. A second was to explore a framework for providing a microcomputer-based decision-making training simulation for teachers. We seek to explore the potential benefits of having results immediately available for a teacher regarding his or her decision-making strategies.

SIMULATION CONTENT

The software was based on the works of Borko[l,9] and Cone[2] because of the general similarities of their studies. Borko [ I] explored teachers’ preinstructional decisions about classroom organization and long-term objectives. Using six binary independent variables-student’s gender, achievement, self-image, social competence, behavior and classroom independence-Borko con- structed 64 scenarios according to a set of rules. With the exception of gender, each independent variable appears in a separate sentence. These sentences are randomly ordered and names, appropriate to gender, are added to the first and third (or fourth) sentences.

Borko’s method was to give the subject a set of scenario cards and have them make various preinstructional decisions about, for example, classroom organization: whether to place a student into a small learning group or to keep the class intact. The subjects could take notes about the scenarios on 3 x 5 index cards and refer to them at any time. For the microcomputer simulation, the teacher sees the scenario on the monitor and enters a decision via keyboard.

148 DEAN M. OISBOID et al.

Cone[2] focused on teachers’ decisions in managing student behavior. The independent variables included gender, classroom organization (small groups vs students at their desks), history of deviant behavior (positive vs negative), and type of behavior deviancy (out-of-seat, speaking out, noise, and physical aggression). The corresponding design was a 2 x 2 x 2 x 4 factorial, totalling 32 scenarios.

The method of implementation was similar to Borko’s. Subjects received a set of scenario cards and wrote down their decisions on each card. Because Cone did not provide a clear algorithm for generating scenarios and due to the difficulty of trying to use less than the entire 2 x 2 x 2 x 4 design to reduce the number of scenarios and time needed to complete the simuIation, the design and scenarios were left intact. Consequently, the 32 scenarios that appeared in Cone’s study were directly written into a text file for use in the simulation, however the order of presentation was randomized within each trial of the simulation.

Figure 1 shows two typical scenarios. The top scenario is from Borko. It describes a female student with high achievement, low independence, high self-image, high social competence and inappropriate behavior.

Sample Borko scenario

Beverly recently took a standardized achievement test and received a score about I yr above grade level. She usually does not work without the teacher’s supervision when given seatwork assignments. Beverly seems sure of herself in most situations. She is generalfy we&liked by her classmates. She frequently disturbs the class with inappropriate behavior.

Sample Cone scenario

Dean is a courteous and helpful fifth grader. It is a Wednesday morning in late September. The class is discussing how languages change with time. All of the students are seated at their desks, involved in the discussion. While you are listening to one student, you see Dean hit the student sitting in front of him with a pencil box.

Fig. I

The bottom scenario, from Cone, is a male student with a history of infrequent deviant behavior. However, he has just displayed deviant behavior (physica aggression). The classroom is organized as whole group instruction.

CAPTURING A TEACHER’S DECISION POLICY

Decisions made by teachers are analyzed in the form of a linear regression model. The model “captures” the teacher’s decision “policy”. The technique suggests how, for example, a teacher might use certain pieces of information to reach a decision@]. Borko[2], for example, used 6 (binary) independent variables to produce 64 possible scenarios, The responses to these scenarios were regressed on a set of dummy variables representing variations in the scenarios (information provided). Or the technique can be viewed as a 26 analysis of variance (ANOVA) with the decision as the dependent variabIe. The result is interpreted as the teacher’s decision policy.

The software employs in the simulation program an ANOVA procedure written to analyze specificalty the “fancy” (fractional) factorial designs (see Abedi and Shavelson[lO]) used in the simulation. Results are presented in the form of the proportion of variance accounted for by each piece of information provided to the tester (eta coefficients). The magnitude of these coefficients are interpreted as representing the underlying decision making policy of the teacher.

TEACHERS’ IDEAL DECISION MODEL

In addition to capturing the teacher’s decision policy, the simulation also obtains data on the teacher’s ideal model; how the teacher ideally weights the information about students given the independent variables of interest, The teacher’s ideal model for the simulation, however, may not necessarily reflect the teacher’s “true” ideal model. For example, the independent variables included for the program may not encompass all the variabfes the teacher holds important. The ideal mode1

Capturing teachers’ decision-making poticies L49

therefore should be viewed in light of the independent variables used in the simulation. Nevertheless. pilot studies conducted by Borko[i] and Cone[Z] showed that the variables included in the simulation tended to be those the teachers considered most important.

In the current version of the simulation, teachers provide their ideal model before making decisions about simulated students. Consequently, the teachers may attempt to make their actual decisions conform more to their ideal than they would in practice. However, in our pilot work, we have not observed this. An alternative would be to ask for the ideal mode1 after the scenarios. But then the model may not be an ideal; it may result from the immediate experience with the scenarios. Further research on this is needed.

SIMULATION DESIGN: FRACTIONAL FACTORIALS

Borko[9] noted that subjects cannot accurately judge 64 scenarios due to fatigue and lapses in attention. Incorporating fractional factorial designs into the simulation permitted us to present part of the complete design, and still estimate the effects of the independent variables on teacher’s decision. The main program allows the person to choose 8 (l/S fraction), 16 (l/4 fraction), or 32 (l/2 fraction) scenarios out of the 64 possible scenarios based on Borko. Cone’s[2] design (2’ x 4) does not allow for substantial reduction in the number of scenarios needed to estimate effects; subjects have to complete all 32 scenarios.

In using fractional factorials, the highest-order interaction and some of the higher-order (4 and 5 way) interaction(s) can be confounded with main effects and lower-order interactions be- cause we assume that they are not of substantive significancefl1,12]. For example, in a 26 design, a l/2 fraction design might confound or “alias” the highest order interaction (the &way interaction) with the main effects (see Table 1). However we assume that this confounding is of no real substantive importance, an assumption confirmed in decision research (e.g. /8]).

A problem with fractional factorial designs lies in the potential interpretability of the results. The smaller the fractional design, such as an I/S fraction design, the greater the confounding of interactions, and the less tenable our assumption that “aliases” are unimportant. For example, suppose two teachers responded to a l/S fraction of Borko’s scenarios and made decisions for 8 scenarios concerning classroom organization. The first teacher answered according to a strict achievement (main effect) perspective; she based her decisions only on the achievement variable and gave ratings of “I” and “5” when student achievement was hgh and low respectively. The second teacher made decisions based solely on the interaction of achievement and gender, giving a rating of “I” for maIes with high achievement and females with low achievement, and a “‘5” for males with low achievement and females with high achievement.

The simulation for the teacher whose decisions were based solely on achievement correctly captured her policy, showing that the achievement variable accounted for 100% of the variance in her decisions (q = 100). Yet the simulation for the teacher who made decisions according to a combination of achievement and gender attributed 100°/o of the variance in his decision to social competence! The reason for this gross error can be found in Table 2. The confounding of main effects and interactions in a l/8 fractional design is such that the gender and achievement interaction is aliased or confounded with social competence.

With this particular l/8 fraction, there is no way to safely distinguish the achievement by gender interaction from the social competence main effect. Though the teacher made “configured” decisions according to a combination of achievement and gender, the results falsely suggest that

Table I. Subject’s q-coefficients as related to decision style for a 1 8 fractional factorial design

Eff@XS Achievement Achicvemcnt~ Gender

A (Gender) 0 0 B (Behavior) 0 0 C (Independence) 0 0 D (Social competence) 0 100 E (Self-image) 0 0 F (Achievement) 100 0 AB 0 0

150 DEAN M. OISBOID et al.

Effects

Table 2. Effects and aliases for 118 fractional factorial design

Aliases

A (Gender) B (Behavior) C (Independence) D (Social competence) E (Self-image) F (Achievement) AB interaction

CE. DF. BCD. BEF. ABCF. ABDE. ACDEF CF. DE. ACD. ACF. ABCE. ABDF, BCDEF AE. BF. ABD. DEF. BCDE. ACDF. ABCEF BE. AF. ABC, CEF. ACDE. BCDF, ABDEF AC, BD. ABF. CDF, ADEF. BCEF. ABCDE AD, BC. ABE. CD& ACEF. BDEF, ABCDF CD. EF. ACF. ADE. BCE. BDF. ABCDEF

Table 3. Subject’s q coefficients as related to decision style for a i’4 fractional factorial design

Effects Achievement Achievement/Gender

A (Gender) 0 0 B (Behavior) 0 C (Independence) 0” 0 D (Social competence) 0 0 E (Self-image) 0 0 F (Achievement) 100 0 AB 0 0 AC 0 0 AD 0 0 BC 0 0 BD 0 100 CD 0 0 DE 0 0 ACD 0 0 ACF 0 0

the teacher made judgements based on the student’s social competency skills. For this reason, we recommend using the shorter set of scenarios only for demonstration or practice, paying little or no attention to the statistical results.

With a t/4 reptication design, the results improve slightly, as shown in Table 3. The results for the “achievement” teacher still come out the same. But now the “interaction” teacher has all of the variance appearing under the wrong interaction, the interaction between behavior and social competence.

Table 4 shows the aliases in a l/4 fractional factorial design. The gender and achievement interaction is confounded with the behavior and social competence interaction. One interaction cannot be distinguished from another with this design! In general, research has shown that three-way and higher-order interactions usually have a negligible effect on decisions and are difficult to interpret substantively.

The confounding in the l/Z fractional factorial design does not wreak such havoc. Table 5 shows the captured policies of the two hypothetical teachers. Again the results for the “main effects” teacher are correct. Now, also, the policy of the second teacher is correctly captured.

At this point, we realized that we are on the horns of a dilemma. The cost of efficiency and attention to task (8 scenarios) is ambiguity, while the cost of increased certainty may be boredom

Table 4. ERects and aliases for l/4 fractional factorial design

Effects Aliases

A (Gender) BCE. BDF, ACDEF B (Behavior) ACE. ADF. BCDEF C (Independence) ABE. DEF. ABCDF D (Social competence) ABF, CEF, ABCDE E (Self-image) ABC, CDF, ABDEF F (Achievement) ABD, CDE. ABCEF AB CE. DF, ABCDEF AC BE, ADEF. BCDF AD BF, ACEF. BCDE BC AE. ACDF, BDEF BD AF, ACDE, BCEF CD EF. ABCF, ABDE DE CF. ABCD, ABEF ACD AEF, BCF, BDE ACF ADE. BCD. BEF

Capturing teachers’ decision-making policies is1

Table 5. Subject’s v coefficients as related to decision style, and aliasing for a I 2 fractronal factorial desian

Effect Achievement Achievemcnt.Gender

A (Gender) 0 0 B (Behavior) 0 0 C ~tode~~~eoce) 0 0 D (Social competence 0 0 E (Self-image) 0 0 F (Achievement) loo 0 AE 0 0 AC 0 0 AD 0 0 AE 0 0 AF 0 100 BC 0 0 BD 0 0 BE 0 0 BF 0 0

Alias

BCEDF ACDEF ABDEF ABCEF ABCDF ABCDE CEDF BDEF BCEF BCDF BCDE ADEF ACEF ACDF ACDE

NOTE: Some two-way and all three-way interactions have been excluded

and lapses of attention. The user is forewarned that if there is a good reason to believe that com- plex decision polices are common (an unusual occurrence in policy capturing research), l/4 and l/2 fractions shoufd be used even though i/8 fractions appeaf in their rapid feedback. Also, substantivefy, the main effects to the two-way interactions represent the boundaries of useful info~ation for the teacher. Accordingly, the sacrifice of the three-way and other interactions to obtain additional fractional designs does not seem extreme.*

COMPUTER AND SIMULATION

An IBM PC XT with 640K of random access memory microcomputer system was used for program development. The software was written by Dean Oisboid and Jamal Abedi in Microsoft BASIC and compiled using the Microsoft QuickBasic Compiler. The software consists of two programs and two text files. One program, BORGEN, randomly constructs 64 student scenarios according to Borko’s set of rules and places them into a random access text file called “BRK”. (Cone’s scenarios were directly written into a random access text file called “CONE32”.) This program then loads and runs the main program. The MAIN program (described below) can be accessed directly without having to run the scenario generator first as long as the two text files, “BRK” and “CONE32,” are on the disk.

The design of the MAIN program is shown schematicaffy in Fig. 2. The flowchart describes all the screens the user sees and the flow of the program. Upon inning MAIN, the title screen asks the user to press (SPACEBAR) to run a demonstration of the simulation or any other key to start the actual simulation.

If the subject decides on the demonstration, she is fed through a “fixed” simulation. Pre-chosen responses have been set in a scaled-down version of the simulation. The teacher need only press a key to step through the demonstration. At the end of the sample simulation, the subject can choose to quit, re-run the sample program, or begin the main program.

Assuming the person hits any other key than the (SPACEBAR), the program asks for the person’s name and an ID number. This info~ation is primarily for naming the text files that save the subject’s responses and the statistical analyses of them. Many subjects can be run, and the program will automatically store the individual results in separate files.

Next the teacher chooses the type of decision she wants to make. The choice of “classroom management” uses scenarios from Cone. The decisions will focus on a teacher’s strategy for dealing with a student’s misbehavior. The choices range from no response to a severe response (“Remove the student from the class on a long term basis”).

*A possible solution to the fractional factorial problem is to use a ” +” and *‘-” method to denote the binary levels of the independent variables, instead of scenarios. This may reduce fatigue, allowing for subjects to go through the complete set of 64 scenarios. A question for future experimentation is whether the ( f / -) system is as informative as scenarios for the subjects.

DEAN M. OISBOID et 01.

Quit or try again

Fig. 2. Flowchart of “Teacher’s decision-making”.

The other types of decisions are available from Borko’s research. “Student goal setting” asks the subject to consider academic vs emotional~so~ial objectives for the student scenarios. The ‘“instructional settings” option, in its various forms (sociat science, mathematics, language arts, and science), concerns the structure of the classroom and the responsibility the teacher assigns to the student. The choices range from keeping the class intact to using small groups with the student leading a group.

Having chosen the type of decision, the subject is asked ta provide his ideal model. This is done by rating the independent variables (e.g. student’s past nchievement) separately on a Likert-type scale ranging from I [extremely important in making the decision) to 6 (not considered at all), After each of the independent variables are rated, the user may define a combination of two independent variables (interactions that refiect part of her decision policy and rate that interaction on the &point Likert-type scale. The program then produces a histogram, showing the teacher’s weighting of the individual pieces of info~ation about the student. The user can choose to continue onwards to the scenarios or to modify his ideal decision model.

Once the ideal model is completed, the program asks the user how many scenarios she wants to rate. (If the person had previously chosen “student management” as the decision, this step is bypassed because the number of scenarios is set to 32.) The user can set the number of scenarios to 8, 16, and 32 corresponding to l/8, l/4, and l/2 fractional factorial designs.

In the scenario segment of the program, each scenario is shown once. When the scenarios are all finished, the program performs the appropriate fractional factorial analysis of variance. q-Coefficients are found by taking the resulting sums of squares for each variabie as a percentage of the total sums of squares and are saved in a text file. Also saved are the teacher’s responses to the scenarios and an analysis of variance summary table including sums of squares, mean squares, F-ratios and their corresponding P values.

When the analysis is finished, the program outputs a histogram displaying the teacher’s ideal model against his actual model. Then a difference table is presented which shows the percentage difference between the actual and ideal models for each piece of information (independent variables) and the one combination.*

The final screen of the simulation aflows the teacher to quit or rerun the simulation.

*The table also contains the ideal interaction (if any) and the most significant acrrruf two-way interaction. if the two interactions are the same. there will be a value for the difference between them. Otherwise the values that accompany them represent deviatioas from 0.

Capturing teachers’ decision-making policies 153

EXAMPLE APPLICATION

One of our test subjects was a female kindergarten teacher with 10 yr teaching experience. She went through the simulation a number of times, using the 8 scenario case as practice and then doing a series of 16 scenario trials. As a brief example, we will explore the results from the first of the 16 scenario trials.

She chose to make decisions about setting student goals (reflecting Borko’s work). She felt that her answers would be more varied when having to consider social vs academic objectives, than if she had made classroom management decisions. She rated her ideal model for the information provided in the scenarios as follows: gender received a low rank, student’s independence and behavior rated about medium in importance, and she considered achievement, student’s social skills, and self-image the most important components in deciding about appropriate objectives. After her ratings, she made decisions on I6 scenarios and examined her results.

This graph compares your ideal model to the results of your decisions. Please consult the manual for information on interpreting the graph.

H-Ideal model)-Actual model

Gender Self-image

Social competence Independence

Classroom behavior Achievement

1.4 Combination

I-HHH) Z-}~~)HHHHHH 3-))))HHHHHH 4-)HHHHH 5--)HHHHH

sHHHHHHHHHH>>)>>>))>> 7-))))

0 25 50 75 100 Weight

Fig. 3. Sample output histogram.

Figure 3 shows the output histogram based on her ideal model and the results of her decisions. In general, it appears that her decisions (represented by “)” symbols) approximate her ideal model (shown by “H” symbols). She tended to focus on student achievement and to lesser degrees on gender, student’s self-image and social skills.

To give a numerical representation, Fig. 4 gives the chart of differences between the actual weightings (r,r-coefficients) and the ideal weightings. Positive numbers mean that the actual mode1 results were greater than the ideal model values. Negative number mean the reverse. For achievement, the difference of “25” suggests that the teacher focussed more on student achievement when making decisions than she thought she did (as recorded in her idea1 model). For the variables of student self-image and social competence having negative differences, the teacher appeared not to consider them as highIy as she thought she would have in her ideal model.

Figures 3 and 4 are the results that the subject sees during the simulation. As described earlier, the program also saves the analysis of variance results to disk. Table 6 contains the teacher’s partial ANOVA results; sums of squares and mean squares are not shown here but are saved on disk.

According to the table, the only statistically significant result is that of achievement, accounting for nearly half of the total variance. However statistical significance is not the only criterion, especially because of the low power. Also important is the mesh with the subject’s idea1 model.

1.

:: 4

::

Factors Actual - ideal (%)

Gender 2.5

Social Self-image competence -15 -15 Independence -12.5

Classroom Achievement behavior -12.5 25 1,4 Combination 7.5

Fig. 4. Sample output difference chart,

154 DEAN M. OISBO~D et al

Table 6. Subject’s analysis of variance results for a I 4 fractional factorial design

Effects d.f. F P v

A (Gender) B (Behavior) C (Independence) D (Social competence) E (Self-image) F (Achievement) AB AC AD BC BD CD DE ACD ACF error

I 3.24 0.10 8.14 I 3.24 0.10 8.74 I 3.24 0.10 8.74 I 0.36 0.73 0.97 I 0.36 0.73 0.97 I 17.64 0.00 47.57 I 0.36 0.73 0.97 I 0.36 0.73 0.97 I 3.24 0.10 a.74 I 0.36 0.73 0.97 I 0.36 0.73 0.97 I 0.36 0.73 0.97 I 0.36 0.73 0.97 I 0.36 0.73 0.97 I 3.24 0.10 8.74 9

For example, if the subject’s ideal model consists of heavy emphasis on academic achievement and the actual results show that the variable captured a significant amount of the variance, the difference between the ideal and actual model may be small. In this case, the information that achievement is statistically significantly may not be as important as the relative information (comparing the ideal mode1 to the actual results). The ANOVA data, though, is valuable for research purposes.

Based on her results, the teacher in subsequent trials changed her ideal model to look more like the actual results shown above. She rated achievement the highest, gave the student’s social competence and self-image variables slightly lesser values, and the remaining factors a rating of “no influence”.

CONCLUSION

The simulation not only explores teacher’s decision-making processes, but demonstrates the feasibility of adapting a useful, though paper-intensive, experiment to microcomputer. By eliminating the majority of the paperwork, the computer simulation allows for the unique advantage of immediate feedback for both the teacher and the researcher or trainer. It also reduces the time needed to prepare materials for the subjects and frees the researcher to focus on methodological considerations (such as exploring the hazards of using fractional factorial designs). Though the programming of the simulation requires time and careful thought, the process forces the researcher to think out all aspects of the simulation. Hopefully, the time involved in programming and testing will be regained by such benefits as immediate feedback and the ability to change aspects of the experiment by modifying the software.

For the teachers and others using the software, there may be a learning potential regarding how one makes classroom decisions. On finding discrepancies between the ideal model and the simulation results, the person may decide to change his/her ideal and try more decisions until the discrepancies are considered meaningless. Using a computer-based simulation allows for such quick exploration.

REFERENCES

I. Borko H. (1978) Factors contributing to teacher’s preinstructional decisions about classroom management and long-term objectives. Unpublished doctoral dissertation, University of California, Los Angeles.

2. Cone R. E. (1979) Teacher’s decisions in managing student behavior: a laboratory simulation of inter-active decision making by teachers. Unpublished doctoral dissertation, University of California, Los Angeles.

3. Mitchell P. D. (1978) EDSIM: a classroom in a computer for lesson planning practice. In Perspectices on Academic Gaming and Simularions I & 2 (Edited by Megardy J.), pp. 191-204. Kogan Page, London.

4. Lloyd S. and Idol-Maestas L. (1983) The use of computer simulation in teacher education. Tease 6(3). 179-185. 5. Strang H. R. and Loper A. B. (1983) Microcomputer-based simulation in training elementary teachers. Educ. Technol.

23, 30-3 1. 6. Russo N. A. (1978) Capturing teachers’ decision policies: an investigation of strategies for teaching reading and

mathematics. Paper presented at the Annual Meeting of the American Educational Research Association, Toronto. Canada.

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7. Shaveison R. J., Caldwell J. and Izu T. (1977) Teachers’ sensitivity to the reliability of information in making pedagogical decisions, Am. E&c. Res. J. 14(Z), 83-97.

8. Shavelson R. J., Webb N. M. and Burstein L. (1986) Measurement of teaching. In ~and$oak o_fResearch on Teaching: Vol. 3. Macmillan, New York.

9. Borko H. (1978) An examination of some factors contributing to teachers’ preinstructional class-room organization and management decisions. Paper presented at the Annual Meeting of the American Educational Research Association. Toronto, Canada.

IO. Abedi J. and Shavelson R. FRACTION: a computer program for fractional factorial design. Behav. Res. Meth., Instrum. Comput. In press.

Il. Kirk R. E. (1982) E.~perimenta~ Design: Procedures for the Beh~~oral Sciences, 2nd edn. Brooks/Cole, Belmont. 12. Dey A. (1985). Orthogonal Fractional Factorial Designs. Wiley, New York. 13. Shaveison R. J. (1976) Teacher’s decision making. In The Psychology of Teaching Metho& (Edited by Gage N. L.).

Yearbook of the National Society for the Study of Education. University of Chicago Press, Chicago.