Evaluating Teaching in Higher Education. September Bruce A. Weinberg The Ohio State University *, IZA, and NBER

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1 Evaluating Teaching in Higher Education September 2008 Bruce A. Weinberg The Ohio State Univerity *, IZA, and NBER Belton M. Fleiher The Ohio State Univerity * and IZA Maanori Hahimoto The Ohio State Univerity * Forthcoming in The Journal of Economic Education Abtract Thi paper develop an original meaure of learning in higher education, baed on grade in ubequent coure. Uing thi meaure of learning, thi paper how that tudent evaluation are poitively related to current grade but unrelated to learning once current grade are controlled. It offer evidence that the weak relationhip between learning and tudent evaluation arie, in part, becaue tudent are unaware of how much they have learned in a coure. The paper conclude with a dicuion of eaily-implemented, optimal method for evaluating teaching. We are grateful for comment from Tiha Emeron, Eric Fiher, and Hajime Miyazaki, eminar participant at Ohio State Univerity and epecially member of The Ohio State Univerity Undergraduate Economic Society, and participant at the 2007 American Economic Aociation Meeting and the 2007 NBER Higher Education Program Meeting. We are alo grateful for detailed comment from the editor, Peter Kennedy and three anonymou referee. We thank Xueyu Cheng,Young-Kyu Moh, and Kent Zhao for able reearch aitance and John-David Slaughter aitance with data aembly and the Regitrar at Ohio State Univerity for data. Keyword: Efficiency, Human Capital, Productivity * Department of Economic; Ohio State Univerity; 1945 North High Street; Columbu, OH

2 I. Introduction and Background Given the difficultie aociated with accurate aement of teaching effectivene, tudent evaluation traditionally have been the primary, if not the only, mean of aeing teaching in higher education. 1 With the Miller Commiion focu on accountability in higher education, evaluation method are receiving increaing public attention (United State Department of Education [2006]; Golden [2006]). While aement i never eay, the widerange of ubject taught make aement in higher education particularly difficult. Thi paper develop a procedure for optimally aeing intructor quality that i eaily implemented. Thi procedure applie baic regreion technique to data that are already available in machine-readable form at mot or all intitution. It i baed on a unique meaure of the learning acquired in a ection of a coure baed on the grade that the tudent in that ection receive in ubequent coure in the ame ubject (adjuting for tudent characteritic). The availability of a learning meaure allow u to invetigate how tudent reward intructor for learning a well a for the grade the receive in their current coure. Economit and educational pychologit both have tudied how grade and learning are related to tudent evaluation of teaching but in a way that prevent one from inferring whether tudent reward higher grade, more learning, or both. Intructor are expected to give higher grade to better performing tudent, making it neceary to control for both grade and learning when aeing the determinant of tudent evaluation. Surpriingly, we are unaware of tudie that have conidered both factor together. While the exiting literature ha not convincingly demontrated that tudent reward learning and not grading leniency, even without thee iue, tudent evaluation generate an incentive for intructor to inflate grade and almot urely place too little weight on learning. There i a ubtantial literature on evaluation of teaching in educational pychology, 1 White (1995) report that U.S. economic department predominantly ue SET to meaure teaching effectivene. He note that there appear to be trong reluctance to rely on direct obervation of teaching, particularly among reearch-oriented department. 1

3 compriing thouand of item (Feldman [1997]). Fortunately there are a number of large-cale literature review. Thi literature focue on the relationhip between tudent evaluation and variou meaure of learning and generally find a trong relationhip. The mot peruaive evidence for a link between learning and evaluation come from multi-ection coure with common yllabi and exam (Cohen [1981]; Dowell and Neal [1982]; Marh [1984, 1987, 2006]; Abrami, d Apollonia, and Roenfield [1997]; Feldman [1997]; and Theall and Feldman [2006]). The lack of intructor dicretion in thee coure raie quetion about the extent to which thee reult will generalize to other coure. More importantly, thi deign i not uitable for determining how grade and learning eparately affect evaluation becaue there i little if any variation in grade conditional on learning. Ironically, dicuion with our tudent ugget that they often etimate how much they have learned in a coure from the grade that they expect to receive. 2 If o, in multi-ection clae, tudent etimate of their learning will be highly correlated with their grade. The literature ha noted that etimate of the effect of learning on tudent evaluation may be biaed by grade. Becaue the multi-ection deign make it virtually impoible to etimate the effect of grade and learning eparately, educational pychologit have generally relied on indirect method to addre the effect of grading leniency (ee, for example, Greenwald and Gillmore [1997]). 3 The literature on the economic of education i le anguine about the relationhip between learning and evaluation. It ha focued on how grade affect evaluation, providing evidence that high grade raie tudent evaluation and argued that the ue of evaluation may lead to grade inflation. 4 Reult from empirical work linking expected grade to evaluation are 2 Thi view wa expreed at a preentation of thi work at our Undergraduate Economic Society at our univerity at which approximately 30 tudent were in attendance. 3 In the economic literature Sheet, Topping, and Hoftyzer [1995] employ a multi-ection approach. Shmanke [1988] ue grade in a ubequent coure in a two-coure equence, which i related to our approach but much le widely applicable. Neither tudy include current grade. 4 Becker and Watt (1999) criticize economic department for following the herd in their uncritical ue of SET meaure and not applying the ame rigor they require of publihed reearch to the ue and undertanding of teaching-quality urvey intrument to evaluate the performance of their faculty. Kanagaretnam, Mathieu, and Thevaranjan (2003) cite everal article from the Chronicle of Higher Education dealing with the topic of the impact of SET on tudent learning and grade inflation. McKenzie (1975) develop a imple model of conumer 2

4 mixed. 5 Thi paper depart from thi work in two way. Firt, it ue actual coure grade rather than expected grade. While tudent generally do not receive grade until after completing their evaluation, we prefer to ue actual grade becaue expected grade are likely to be quite noiy and tudent have ome idea of what grade they may receive baed on midterm reult, homework core, and other objective information on their coure performance a well a poible ignal from the intructor. Second, unlike mot of the literature, thi paper meaure grade uing the average grade in a ection rather than individual-level grade. In an individuallevel regreion, mot of the variation in grade arie from individual difference in grade within a ection. Therefore, the individual-level relationhip between grade and evaluation indicate whether tudent who are at the top of a given ection give higher evaluation than thoe at the bottom of that ection, not whether intructor who grade more leniently receive higher evaluation. Third, a indicated above, thi paper i alo the firt to tudy how grade and learning are jointly related to evaluation. We ue our analyi of the determinant of tudent evaluation to ugget improved method for evaluating intructor. Educational pychologit have argued that if there i little effect of grade on evaluation but there i a trong relationhip between grade and learning, one would not want to adjut tudent evaluation for grade (Greenwood and Gillmore [1997]). If evaluation were affected by grade, however, it would be adviable to adjut evaluation for grade, provided that one can condition on learning. Such an adjutment would reduce intructor incentive to inflate grade and remove a ource of bia in evaluation. There are two additional factor that arie when relying on tudent evaluation. Firt tudent evaluation depend on tudent aement and valuation of how much they have choice in which the ue of SET by academic intitution provide an incentive for intructor to alter the gradeeffort tradeoff that tudent face to make it eaier (le cotly in term of effort) to earn higher grade. Thi contribute to grade inflation and adverely affect the intitution ability to ditinguih good and bad tudent. A earch of the Chronicle table of content for the key word tudent evaluation yield 22 article and note for the year 2005 (through the end of October). A earch for both tudent evaluation and grade inflation yield ix letter and article between 1998 and 2005, for example, Benton (2004). 5 Nichol and Soper [1972]; Krautmann and Sander [1999]; Boex [2000]; and Kelley [1971] report a poitive relationhip between expected grade and evaluation, while DeCanio [1986] and Nelon and Lynch [1984]; and 3

5 learned in a coure. Below, we provide evidence that tudent evaluator may not be well poitioned to make that determination accurately. Even if tudent accurately ae their learning, they may place le weight than univeritie do on learning relative to the coure experience. For example, ociety and parent may place higher weight on human capital production and le weight on the coure experience than tudent do becaue tudent dicount at a high rate or becaue human capital generate externalitie for ociety. In either cae, relying olely on tudent evaluation will ditort intructor incentive away from the ocial optimum. Thi paper fit into an emerging literature on the determinant of outcome in higher education (ee Bettinger and Long [2004]; Beddard and Kuhn [2005]; and Hoffmann and Oreopoulo [2006]). It alo relate to a large literature in the economic of education on the determinant of tudent outcome in primary and econdary education. Our data cover nearly fifty thouand enrollment in almot four hundred offering of principle of microeconomic, principle of macroeconomic, and intermediate microeconomic over a decade at The Ohio State Univerity. 6 In addition to information on tudent evaluation, the data contain all grade that tudent received in ubequent economic coure and rich information on tudent background, including race, gender, ethnicity, high chool cla rank and SAT and/or ACT core. Thee data can be ued to regreion-adjut grade and our learning meaure. The data how a trong poitive relationhip between tudent evaluation and both current grade and learning when thee variable are included eparately, but when thee variable are included in the ame model, the current grade i related to tudent evaluation but learning i not. There are many potential explanation for thee reult, including a variety of election argument. We devote coniderable effort to five of them, concluding that, on average, tudent are not aware of how much they have learned in a cla. There are no reaon to believe that the focu on current grade and uncertainty about learning i pecific to economic or the Bohardt and Watt [2001] report weak, negative, or mixed reult. 6 Thee coure were choen becaue they are tandard, they enroll the mot tudent, and more of the tudent in thee clae take additional economic clae. Thee were the only clae for which data were collected or analyzed. 4

6 intitution tudied and therefore our reult are expected to generalize, at leat qualitatively. The availability of a meaure of learning and the check that we run on it entail a number of analye that are of interet in their own right. For example, we alo conider how intructor characteritic are related to meaure of the quality of teaching. In ome cae, female and foreign-born intructor receive lower tudent evaluation than male and US-born intructor. Learning, however, i not ytematically related to intructor gender or national origin, nor are there ytematic difference in evaluation or tudent learning between non-tenure track faculty and tenure track faculty. Thi finding i noteworthy in light of Ehrenberg (2004) obervation that little i known about the effect of part-time and non tenure-track faculty on tudent learning and other meaure of academic production. While we do not find that obervable intructor characteritic are related to learning, we do find large variation acro intructor in learning performance. Thi reult i conitent with evidence from primary and econdary education (See Rivkin, Hanuhek, and Kain [2005]). We alo invetigate whether tudent in ection that rate their intructor more highly are more likely to take additional clae, a revealed-preference meaure of quality (ee Hoffmann and Oreopoulo [2006]). We find that tudent evaluation of teaching are unrelated to the number of ubequent economic clae that tudent take, further eroding our confidence in tudent evaluation. 7 II. Data The data et include tudent who took principle of microeconomic, principle of macroeconomic or intermediate microeconomic at The Ohio State Univerity between 1995 and 2004, and contain identifier for the ection the tudent took, tudent demographic characteritic, and grade in all economic coure taken during thi period. They alo contain rich background information on tudent, including race, gender, ethnicity, high chool cla 7 Two obviou additional meaure are drop rate and wait lit. Wait lit are uncommon in thee coure and the meaure of drop rate include tudent who dropped before the beginning of the cla a well a thoe who dropped once the coure began. 5

7 rank and SAT and/or ACT core that can be ued to regreion-adjut grade and our learning meaure. In the regreion, ACT core are included for tudent for whom we have them and SAT core are included for tudent for whom we do not have ACT core (becaue ACT core are available for more tudent). The etimate include a dummy variable for which core i included. We obtained data on all ubequent economic coure taken by thee tudent through the end of academic year Student evaluation are anonymou and are available at the ection-level but not at the tudent-level. Thu, we etimate the relationhip between grade and evaluation at the ection level rather than at the individual level, which i appropriate, a dicued above. The evaluation intrument contain ten item, including an overall core, which i the principal meaure of tudent evaluation ued in thi tudy. Other quetion include meaure of perceived learning, preparation and organization, the intructor attitude, and the extent to which the coure timulated tudent to think. Table 1 how the variable definition and their mean and tandard deviation for the three et of coure. The data et comprie 190 ection (with 26,666 tudent) in principle of microeconomic; 119 ection (with 14,729 tudent) in principle of macroeconomic; and 86 ection (with 4,111 tudent) in intermediate microeconomic. The average evaluation core range from 3.72 (tandard deviation of.54) for principle of macroeconomic to 3.86 (tandard deviation of.44) for principle of microeconomic on a cale of 1 (lowet) to 5 (highet). The average coure grade i cloe to 2.7 on a four-point cale (with a tandard deviation of about.3), a B-, for all three coure. The table how the ditribution of intructor and tudent characteritic for the three coure. III. Etimation We employ a three-tep trategy to etimate grade and learning and their relationhip to tudent evaluation: (i) we firt etimate grade (ii) then we etimate the amount of learning in each ection baed on grade in ubequent ection; (iii) finally we ue thee learning etimate to ae how grade and learning are related to evaluation. In addition, we analyze thee 6

8 learning meaure ince they are of interet in their own right. Thi ection decribe the procedure tep-by-tep in term of principle of microeconomic, including how individual-level data on current and ubequent grade are collaped to the ection-level to be merged into ection-level evaluation. Similar procedure were ued for principle of macroeconomic and intermediate macroeconomic. Step 1. Etimating Grade Let i index tudent and index the bae ection (i.e. the particular ection of principle of microeconomic that the tudent took). Let took bae ection. In the firt tep, variable g i denote the grade received by tudent i who g i i regreed on a vector of bae ection dummy D v i and the tudent characteritic at the time of the bae ection, X v i. The pecification i v v v v gi = X iβ 1 + Diψ + ε1 i (1) The coefficient ψ on the dummy variable for bae ection give the mean grade in the ection adjuting for individual characteritic. Thee coefficient are ued in the third tage to repreent grade. Step 2. Etimating Learning Grade in ubequent coure are ued to meaure learning. Let j index ection of ubequent economic coure, o that g ij denote the grade of tudent i, who took bae ection, in ubequent ection j. Grade in ubequent coure, g ij, are regreed on a vector of dummy variable for the ubequent ection (to control for difference in grading acro clae), Z v ij ; a vector of dummy variable for the bae ection, D v ij ; and tudent characteritic, X v ij, at the time of ection j. Formally, v v v v v gij = X ij β 2 + Z ijγ + Dijθ + ε 2ij v (2) The coefficient θ on the dummy variable for tudent who took bae ection indicate how well thee tudent do in later coure adjuting for their characteritic. Thi coefficient i our 7

9 meaure of learning, or human capital produced in ection. The θ we etimate in thi tage are ued in our third tep to control for learning produced in the ection and the etimate are of interet in their own right. Step 3. Evaluating Student Evaluation Having etimated ψ, the grade in bae ection and θ, the learning in bae ection, we now regre the tudent evaluation for bae ection, e, on learning, grade, and intructor and ection characteritic, W v : v v e = θ φl + ψ φg + W ρ + ε 3. (3) The coefficient φ l indicate how much tudent value learning (net of any cot of learning) and the coefficient φ g, how much tudent value high grade when evaluating the intructor. The coefficient vector ρ v tell how obervable intructor and ection characteritic are related to evaluation. Additional Analye Once we obtain a meaure of learning, we can etimate a variety of related effect. We invetigate the effect of intructor characteritic uch a gender, native language, tenure track tatu, or whether or not the intructor i a graduate teaching aociate, on learning by etimating, θ r v W + u. (4.1) = 'β 3 A above, W would repreent the characteritic of ection, including thoe of the intructor. We alo ae how intructor characteritic are aociated with grading leniency, by etimating, ψ v ' v +. (4.2) = W β 4 + θ γ ξ One could etimate thi model with or without θ a a control for the effect of human capital. 8

10 IV. Finding Validity of the Learning Meaure We begin by validating the learning meaure. The mot obviou concern i that it i noiy becaue of ampling error. To addre thi poibility, we etimate the hare of the variance in the etimate of learning in each ection that i due to learning in the ection a oppoed to ampling error. To do thi, we plit each cla into two equally-ized halve and calculate the covariance between learning in each half. Intuitively, the idioyncratic component of learning in each half of the coure will be unrelated to each other, and the covariance between them will give the variance in learning in the ection a a whole. We now dicu the formal procedure. Let θ μ + ε j = denote the etimate of learning for portion { 1,2} j 9 j of ection, which equal the learning in ection, μ, plu ampling error in portion j of the ection, ε j. We etimate the covariance between the two randomly aigned halve of each ection, Cov( θ 1,θ 2 )= Cov( μ 1,μ 2 )+Cov( μ 1,ε 2 )+Cov( ε 1,μ 2 )+Cov( ε 1,ε 2 ). The term involving the ampling error, ε j drop out becaue they are orthogonal to the other component by contruction, and the covariance between the μ give it variance. Formally, 1 2 ( θ ) Var( μ )2 1, 2 1 Cov θ =. Thi meaure give the tandard deviation in learning acro ection becaue it repreent the variation in learning for tudent who took a particular ection in the abence of any ampling error. We alo calculate the hare of our learning meaure that repreent learning a oppoed to ampling error by calculating ( θ θ 2 ) ( θ ) Var( μ ) ( μ ) + Var( ε ) Cov, 1 = Var Var. Here ε denote ampling error in the entire ection. Second, in regreion (2), where we generate our learning meaure by relating future grade to future ection dummy variable and bae-ection dummy variable, we tet for the tatitical ignificance of the bae-ection dummy variable (vector θ ) which meaure baeection learning. Third, we regre the bae-ection dummy variable from (2) on intructor

11 dummy variable. The econd-tage model i given by θ = φi + u, where I denote a vector of dummy variable for the intructor teaching the bae ection. It eem reaonable to aume that learning varie acro ection and acro intructor. Under thi aumption, ection dummy variable and intructor dummy variable are expected to be tatitically ignificantly related to the learning meaure (i.e., future grade). Table 2 report reult for the three coure. A hown in the top panel, there i ubtantial variation in learning acro ection the tandard deviation range between.15 and.2 grade point. Thee difference imply that moving a tudent from a ection with the mean level of learning to one with learning one tandard deviation above the mean would raie hi grade in all future economic clae from, ay from a B- to more than half way to a B. Moreover, between 46% and 83% of the variance acro bae ection in the learning meaure i due to learning at the ection a oppoed to noie, o our etimate of learning are quite precie. In other word, while (unoberved) individual factor uch a motivation and ability account for the majority of the variation in future grade acro tudent, only a minority (54% to 17%) of the variation in future grade acro bae ection i due to noie from ampling and the vat majority (46% and 83%) i ignal. When we etimate (5), including control for ection characteritic, F-tet for the joint ignificance of the bae-ection dummy variable oundly reject the null hypothei that baeection grade are not important determinant of learning. For all three coure, the P-value are le than A hown in the lower panel, more than half of the ection-learning effect for principle of macroeconomic are due to intructor effect. Intructor account for 44% of the variation in the ection-learning effect for intermediate microeconomic and 39% for principle of microeconomic. The null hypothei of no intructor effect i alo rejected with a P-value le than.0001 for macro-principle and with P-value of.01 for micro-principle and.1 for intermediate microeconomic. While much of the variation acro ection i due to fixed 10

12 intructor effect, there i till coniderable variation within intructor (due to variation in the rate of learning by doing or deterioration and idioyncratic factor). For thi reaon, we focu on ection-level (rather than intructor-level) etimate of learning for mot of the analye. Thee reult indicate that although they contain ome ampling error, grade in future coure are a valuable meaure of learning in bae-ection. The ubtantial variation in learning acro ection and the trong effect of intructor on learning are alo noteworthy and indicate the importance of evaluating intructor baed on the learning that they produce. Principle of Microeconomic Thi ection report etimation reult for equation (3). We begin with reult for principle of microeconomic, and then dicu the reult for principle of macroeconomic and intermediate microeconomic. We then turn to alternative explanation of our reult, including thoe baed on election iue, and conclude with ome additional analye. Thee etimate and other like them include intructor random effect. The firt column of table 3 report a regreion of tudent evaluation on the current coure grade. We find that tudent in ection with higher grade rate their coure more highly than thoe in other ection. Column 2 report a regreion with only the learning meaure, which i alo found to be poitively aociated with evaluation though with a maller coefficient than current grade. When both current grade and learning are included in the ame regreion (column 3), the effect of the current grade dominate, and the coefficient on learning i mall and inignificant. 8 We remind the reader that both current grade and the learning meaure are regreion adjuted for obervable tudent characteritic. The remaining column examine a variety of other potential determinant of tudent evaluation. Firt, we include a et of intructor characteritic without controlling for grade or learning (column 4). Female intructor receive lower evaluation than men, a do foreign-born 8 If learning i multi-dimenional, grade in the current coure may capture learning of material that i valuable outide of future coure, including kill a an economic actor or a citizen. By meauring learning uing grade in future clae, the preent analyi will not capture thee other dimenion of learning. An alternative explanation for the trong relationhip between evaluation and current grade i that teacher who expect to receive bad 11

13 intructor, although thee difference are not tatitically ignificant in all pecification. There are no dicernable difference in evaluation between non-tenure track lecturer, graduate teaching aociate, and tenure-track faculty. Difference in grading practice and learning may be reponible for the gender gap in evaluation a well a the ubtantial (but tatitically inignificant) foreign-dometic gap. To explore thi poibility, column (5) include both current grade and learning along with intructor characteritic. Incluion of thee variable increae the gender and foreign-dometic gap in evaluation lightly. The above evidence ugget that tudent rate women and perhap foreign intructor le favorably than other, poibly reflecting ditate/direpect for uch intructor or unmeaured difference in the coure experience like language ability or teaching tyle. The regreion in column (6) include ection characteritic; column (8) report etimate with all of thee variable, year dummy variable, and the repone rate for the evaluation in the ection. In both regreion, the coefficient on the current coure grade i ignificant and imilar in magnitude. To ummarize other tatitically ignificant finding, column (6) how that tudent in honor and evening ection give higher evaluation than other tudent (i.e. thoe in non-honor, daytime ection). Column (7) how that the coefficient for honor and night clae a well a for female intructor all are ignificant after fully controlling for the available variable. The foreign effect remain large, but inignificant. The etimate in Table 3 conitently how a tatitically ignificant effect of the current coure grade. Indeed, the coefficient become larger a more variable are controlled. According to column (8), a one tandard-deviation change in the current coure grade i aociated with a large increae in evaluation over a quarter of the tandard deviation in evaluation. Once current grade are controlled, learning, a meaured by future grade, i not tatitically ignificantly related to evaluation in any of the regreion. Our ue of the actual current coure grade a a meaure of the expected grade in the evaluation penalize their clae with harder exam or a harder curve. 12

14 coure deerve ome dicuion. A noted already, tudent likely have ome idea of what grade they will receive baed on formal or informal feedback received during the quarter. Alternatively, tudent may form expectation of their coure grade baed on the reputation the intructor grading in previou offering of the coure. We examine thi lat poibility by including in the regreion the lagged grade the mean grade in the lat offering of the coure by the intructor along with the current grade. Column (8) preent reult without the lagged grade for the ample for which the lagged grade i available. Including the lagged grade, in column (9) doe not change the etimated coefficient of the current coure grade or learning, and the coefficient for the lagged grade i itelf mall and tatitically inignificant. It appear that tudent bae their evaluation on indication provided by the profeor about the current coure rather than on the profeor reputation (at leat baed on recent offering of the coure). Individual Evaluation Item We ue ten evaluative item, nine focuing on pecific apect of the coure experience a well a the overall core, which ha been the focu of the analyi thu far. Etimate for thee individual item (not reported here, but available upon requet) are quite imilar to thoe for the overall evaluation meaure. The current coure grade i alway aociated with higher evaluation and the relationhip i tatitically ignificant at the 5% level in eight of the ten cae. None of the evaluation item are tatitically ignificantly related to learning. One item directly meaure learning, aking tudent whether they, Learned greatly from intructor? It i noteworthy that thi meaure i no more cloely related to our learning meaure baed on future grade than any of the other item. Thi finding ugget that tudent are not able to evaluate the amount they learn in a coure or that they bae their etimate on the grade that they expect to receive. Principle of Macroeconomic and Intermediate Microeconomic Thi ection report reult for principle of macroeconomic and intermediate microeconomic a a check. There are only 60 percent a many macro-principle ection a there are micro-principle ection and fewer of the tudent in macro-principle take ubequent 13

15 clae. Similarly, there are fewer intermediate economic ection than micro-principle (le than half a many) or macro-principle (three quarter a many) and fewer tudent take later clae making the etimate noiier. 9 Thu, both et of etimate will be more noiy than thoe for principle of microeconomic. The reult, which are reported in Appendix Table 1 in the ame order a thoe for principle of microeconomic, are generally conitent with thoe for principle of microeconomic. For both principle of macroeconomic and intermediate microeconomic, grade in the current coure are trongly related to tudent evaluation for later coure. In fact, the etimate are, if anything, tronger, than thoe for micro-principle. Learning i unrelated to evaluation once current grade are controlled. Again women and foreign born intructor tend to receive lower evaluation than men and dometic intructor, but thee difference are not conitently tatitically ignificant. Reult, not reported here but available upon requet, for individual evaluation item alo how a trong relationhip with current coure grade but not learning. Robutne Thi ection explore two robutne check. One poibility i that learning in principle of microeconomic may be better captured by performance in other microeconomic clae. To explore thi poibility we have etimated learning uing grade in intermediate microeconomic only. The etimate are reported in the top panel of Table 4 and are imilar to the previou etimate, with a trong poitive relationhip between current grade and tudent evaluation and a weak relationhip between learning and tudent evaluation. Becaue we are often intereted in the effectivene of particular intructor, we have alo re-etimated our model uing intructor-level data. To do thi, we etimate the weighted mean of tudent evaluation and of our meaure of grade and learning for each ection taught by an 9 While micro-principle i not a prerequiite for macro-principle, almot all tudent take micro-principle before macro-principle, o that almot all of the grade in the macro-principle clae are available to etimate learning for micro-principle, while for macro-principle, learning can only be etimated for tudent who take a third economic coure. Similarly, many buine major require intermediate microeconomic, but no additional clae. 14

16 intructor etimated in equation (1) and (2). In thee calculation, each ection wa weighted by the number of tudent in that ection. Equation (3) i then etimated at the intructor level with thee intructor-level mean a the independent and dependent variable 10. (In the pecification that include ection and intructor characteritic, the mean of thoe variable are included.) Thee etimate, reported in the bottom panel of the table, how omewhat higher etimate of current grade than the ection-level etimate, but the coefficient learning i mall and tatitically inignificant. Summary The highlight of what we found o far are: 1. There i a conitent poitive relationhip between grade in the current coure and evaluation. Thi finding i robut to the incluion of a wide range of control and pecification. 2. There i no evidence of aociation between learning and evaluation controlling for current coure grade. 3. Learning i no more related to tudent evaluation of the amount learned in the coure than it i to tudent evaluation of other apect of the coure. 4. In ome cae women and foreign-born intructor receive lower evaluation than other intructor, all ele equal. V. Alternative Explanation Through the variou check reported in Table 2, we feel confident that our meaure of learning i valid. Our finding how that tudent evaluation are trongly related to grade and that learning, a meaured by future grade, i unrelated to tudent evaluation once grade have been controlled. The obviou explanation for thee finding i that grading leniency i an important determinant of evaluation and that tudent do not reward intructor who generate learning per e. There are, however, five other explanation that come to mind and thi ection 10 We do not include the lagged evaluation becaue it i ill-defined when thee regreion are run for intructor a 15

17 invetigate them and how that they are unlikely to explain our reult. Firt, the finding may indicate election into coure for example, the leat able tudent may diproportionately take coure from the intructor with the bet tudent evaluation, biaing downward our etimate of learning for the bet intructor. Second, the reult for learning may reflect election into future clae. Our learning meaure can only be contructed for tudent who take ubequent economic clae. Better performing tudent in one economic coure may be more likely to take future economic clae. If more highly rated profeor make economic more attractive particularly for tudent with low economic ability, the relationhip between grade and whether tudent take additional clae will be weaker for tudent taking clae from the highly rated profeor. In thi cae, our learning meaure will be biaed downward for highly rated intructor relative to le highly rated one, leading u to underetimate the effect of learning on evaluation. We will examine thi poibility. A third explanation i that tudent from more highly rated profeor may be induced into taking more difficult future clae. We will alo examine thi poibility. A fourth interpretation i that the cot to tudent in coure where they learn much may offet the benefit they perceive. Latly, tudent may be unable to gauge how much they have learned in their clae. The weak relationhip between our learning meaure and the evaluation item that pecifically addree learning, ugget that the lat explanation may be the mot relevant one. We invetigate thee explanation below. Selection into Bae Section Thi ection conider whether election biae our etimate. There are a number of election argument. The implet i that there may be election into bae ection, o that variation in learning and grade are due to difference in tudent ability. 11 A noted earlier, all the preceding etimate control for a rich et of tudent characteritic including ACT or SAT oppoed to individual coure. Alo, lagged grade did not matter in the previou model. 11 Another election poibility i that tudent who expect to receive bad grade drop clae and therefore do not complete evaluation (Becker and Power [2001]). If, within a cla, tudent expecting lower grade give lower evaluation, elf-election would raie both the oberved average coure grade and the oberved evaluation. 16

18 core and high chool cla rank, thereby mitigating thi concern. For principle of microeconomic, we have alo retricted the ample for which we etimate learning to tudent who took principle of microeconomic in the Fall of their firt year. Thee tudent preumably have little information about intructor. Thi trategy i imilar to Hoffmann and Oreopoulo [2006]. Reult were le precie but imilar to thoe preented above. Selection into Future Clae There are other election argument. For example, the effect of learning, a meaured by future grade, on evaluation may be biaed downward if tudent with low ability in economic take more additional economic coure after taking a coure from a highly rated intructor than after taking a coure from a le highly rated intructor. 12 To tet thi hypothei, we etimate logit model of whether tudent i take ubequent economic coure, Future Cla i. The firt model i, Here e denote the evaluation in ection ; FutureCla i = 1 if e β + X i Γ +W Π +ε i > 0. 0 if e β + X i Γ+W Π+ε i 0 X i denote tudent characteritic; and W denote characteritic of the intructor and ection. In addition to individual characteritic included above, in thee regreion, X i include dummy variable for the college that houe the tudent major and interaction between thee dummy variable and a quadratic in time to account for (time-varying) difference in requirement to take economic clae acro different unit. 13 Thi model can be ued to determine whether tudent take more economic clae after taking a cla from a highly-rated intructor than they do after taking a cla from a le highlyrated intructor, in which cae ˆ β > 0. The econd model i, Unfortunately, our data do not permit u to identify tudent who dropped a coure. 12 Alternatively, tudent who are more intereted in economic may rate their intructor better and continue with economic clae even if they are not a capable. Random variation acro ection in tudent motivation might produce more low-quality tudent going on to take more economic clae when rating are higher. Thee etimate alo tet for thi hypothei. 13 Unfortunately, hitoric information on which program require which economic coure i not available. 17

19 FutureCla i = 1 if g iβ + g i e π + X i Γ +Φ +ε i < 0. 0 if g i β + g i e π + X i Γ+Φ +ε i 0 A above, e denote the evaluation in ection and give the grade received by tudent i in ection and 18 X i denote tudent characteritic; Φ denote a et of ection dummy variable, which are etimated explicitly and account for difference acro bae ection in the probability of taking future coure. With ection fixed effect, the intructor and ection characteritic (including the direct effect of tudent evaluation) are captured by the ection fixed effect. The parameter β give the difference between the probability of taking ubequent economic coure by tudent with higher grade relative to thoe with wore grade. The parameter π, on the interaction between grade and evaluation, i of particular interet. If π > 0 ( π < 0 ), then the relationhip between tudent grade and the probability of taking future economic coure i tronger (weaker) in ection with higher evaluation. While one might have expected that tudent from ection with higher evaluation would be particularly likely to take additional economic clae, the etimate reported in the odd numbered column of table 5 how little relationhip between tudent evaluation and the number of ubequent economic clae taken. Thu, there i no evidence that tudent take more clae after having more highly rated intructor. Viewed from a revealed preference perpective, thi reult cat doubt on tudent evaluation a a meaure of teaching quality. For both principle of microeconomic and macroeconomic, tudent with higher high chool cla rank and better math ACT or SAT core are more likely to take ubequent coure while thoe with higher verbal core and women are le likely to take additional economic clae. The other control are not ytematically related to the probability of taking additional economic clae. The etimate in the even-numbered column of the table how a trong poitive relationhip between grade in the current coure and the number of ubequent economic clae taken for tudent in principle of microeconomic and principle of macroeconomic. The relationhip i negative and ignificant for tudent in intermediate microeconomic coure. g i

20 The relationhip between grade and the probability of taking future coure only depend on evaluation in intermediate microeconomic. Good tudent are particularly likely to take ubequent economic clae after taking intermediate microeconomic from a highly-rated intructor, which would bia the relationhip between evaluation and learning upward. Here too, there i little evidence that election explain the weak relationhip between grade in ubequent clae and tudent evaluation. We alo etimate our learning meaure uing a formal election model. For thee etimate, we look at tudent who took principle of microeconomic a their firt principle coure and their grade in principle of macroeconomic. Our intrument for whether tudent take principle of maroeconomic, which were excluded from the future grade equation from which learning wa etimated, are a et of interaction between the college that houed the tudent major at the time of enrollment in principle of microeconomic and time. Thi i a good intrument, becaue it reflect exogenou change in the requirement of major and adviing practice. We included college dummy variable in the equation for taking principle of macroeconomic and in the grade equation for principle of macroeconomic, o the election model i etimated from variation over time in the hare of principle of microeconomic tudent taking principle of macroeconomic within major. The reult, which are quite imilar to thoe in table 3, are reported in Appendix Table 2. Conitent with the previou reult, there i a trong relationhip between tudent evaluation and grade, which i unaffected by the incluion of learning. The learning meaure i weakly related to evaluation. The Difficulty of Future Coure Taken Another election argument focue on the particular clae that tudent take. Student who take a coure from a more highly rated profeor may take additional clae that are more difficult than tudent whoe prior coure i from a le highly-rated intructor. While the etimate of learning baed on future grade include fixed effect for future coure, if a diproportionately large number of tudent from a particular cla take coure that are more 19

21 difficult in the ene of yielding lower average grade for the ame amount of initial knowledge, it will lead u to underetimate learning from thoe ection. Our data provide a convenient tet for thi hypothei inofar a intermediate microeconomic (the third cla taken by mot tudent) i offered in two verion a tandard coure and a calculu-baed coure, taken by roughly 13 percent of the tudent in our ample. For tudent who took an intermediate microeconomic cla, we etimate uing a logit model. Here Hard Intermediate i = 1 if e β + X i Γ +ε i > 0 0 if e β + X i Γ+ε i 0 Hard Intermediatei i a dichotomou variable equal to 1 if the peron took the mathematical intermediate microeconomic cla and 0 if the peron took the le mathematical intermediate microeconomic cla; e denote the evaluation in ection, and denote the tudent characteritic. The parameter β indicate whether tudent who took ection with higher rating were more or le likely to take the more mathematical intermediate cla. The etimate are reported in Table 6. There i no relationhip between tudent evaluation in principle of microeconomic or macroeconomic and the probability of taking the more mathematical intermediate microeconomic coure. (For principle of macroeconomic the etimate indicate that tudent in more highly rated ection are actually le likely to take the more mathematical intermediate microeconomic coure, although the coefficient i not tatitically ignificant.) Not urpriingly, tudent who have higher math ACT or SAT core are more likely to take the mathematically intenive cla. Thee etimate indicate that the finding of no relationhip between evaluation and learning i not due to thi potential ource of bia. Overall, we conclude that there i little evidence that election (in a variety of form) account for the weak relationhip between evaluation and learning reported above. VI. Determinant of Learning and Grade The preceding etimate how that there i ubtantial variation in learning acro 20 X i

22 ection and that intructor effect account for much of thi variation. Thi ection conider how obervable intructor characteritic are related to the learning meaure and how current grade are related to learning and intructor characteritic. Thee are etimate of equation (4.1) and (4.2). Given that controlling for current grade eliminate the relationhip between learning and evaluation, we anticipate that learning and current coure grade are poitively related. The reult are reported in table 7. The firt three column report reult for principle of microeconomic. They how no ytematic relationhip between intructor characteritic and current coure grade. A expected, grade in the current coure are poitively related to learning, although the relationhip i not tatitically ignificant. Thi finding i conitent with intructor giving higher grade to ection that do better. Not urpriingly tudent in honor ection of the coure received higher grade and learned more than other tudent, and thee reult are highly tatitically ignificant. Reult for principle of macroeconomic, reported in column (4) through (6), how that none of the obervable intructor characteritic are related to learning, but that women tend to give lower grade. A hown in column (5), learning i poitively, but not ignificantly, related to grade in the current coure, but the previou reult are robut to controlling for learning. Reult for intermediate microeconomic, reported in column (7) through (9) how that women tend to aign higher grade, but none of the other intructor characteritic i tatitically ignificantly related to current grade. Student who took intermediate microeconomic from a foreign-born intructor learn le. Otherwie, none of the obervable intructor characteritic i tatitically ignificantly related to learning. A before, the current coure grade are poitively related to learning. It i noteworthy that Table 2 howed large intructor difference, but that the etimate in Table 7 how no conitent relationhip between obervable intructor characteritic and learning. Thi finding parallel the literature on teacher effect in primary and econdary chool, where teacher effect are found to be large, but obervable teacher characteritic have only weak effect (ee, for example, Rivkin, Hanuhek, and Kain [2005]). Thu here, a in that 21

23 literature, the characteritic of intructor that matter the mot are unobervable. VII. Optimal Evaluation Criteria Thi ection conider optimal evaluation criteria. Aume that evaluation depend on grade, g ; learning, l ; and an unoberved coure experience, ξ, according to e = φ g + φ l + ξ (3 ) g l which i equivalent to (3) without intructor characteritic. If a direct meaure of learning and unbiaed etimate of φˆ g and φˆ l are available from (3 / 3 ), it i poible to etimate the coure experience directly from, ( ˆ φ g ˆ φ l ) xˆ = e (5) g l The ˆ x in (5) are the reidual from (3 / 3 ). In other word, our meaure of the coure experience i evaluation regreion-adjuted for grade and learning and (perhap) intructor characteritic. 15 With thi information, intructor can be evaluated on the coure experience they provide and the amount of human capital they produced. With a ene of ocial prioritie, etimate of the coure experience, human capital, and grade, adminitrator can reward intructor baed on ocial welfare. Becaue much of the interet i in evaluating intructor, we analyze ranking of intructor for each coure. 16 Figure 1.A how a trong poitive relationhip between the etimated coure experience for intructor and hi or her tudent evaluation for principle of 14 We note that that thi procedure attribute all factor that affect evaluation other than grade and learning to the coure experience. If, a we have heard mentioned from time to time, intructor give out cookie or the like when adminitering evaluation, that will be attributed to a better coure experience. Thu, in only regreion adjuting evaluation, we may be placing too much emphai on them, but we note that unadjuted evaluation are affected by thee biae and our procedure ha the advantage of adjuting evaluation for grading leniency. 15 A noted by the editor the deirability and feaibility of controlling for intructor characteritic i not traightforward. For intance, one might want to adjut for gender to eliminate a gender-bia in evaluation, but it i not clear whether one would want to control for, ay, experience. 16 The coure experience for each intructor i the mean of hi or her reidual from regreion (6) in Table 3, which control for learning, grade, and ection characteritic. The learning produced by each intructor (regreion adjuted) i the mean of each intructor reidual of a regreion like that in column (6) of Table 3 where learning i the dependent variable and ection characteritic are controlled. 22

24 microeconomic. The correlation between the two variable (hown in Table 8) i.94 indicating that regreion-adjuting evaluation for grade and learning leave mot of the information intact. Panel B plot learning againt tudent evaluation howing virtually no relationhip between the amount of learning an intructor produce and hi or her tudent evaluation. Given that learning and evaluation are eentially independent, it i not urpriing that many of the intructor who do well on their evaluation do poorly on learning, while many of the intructor who do poorly on their evaluation produce a lot of learning. Thu, tudent evaluation provide no meaningful information about learning. The table alo how that the correlation between an intructor coure experience and the amount of learning produced i low,.11. The remaining panel of the table how correlation for principle of macroeconomic and intermediate microeconomic. Again, there i a trong relationhip between the imputed coure experience and tudent evaluation, but not between learning and tudent evaluation. In fact, in thee coure, learning i negatively related to evaluation and the coure experience, epecially for principle of macroeconomic. The preceding reult have two implication for the deign of optimal evaluation criteria. Firt, ranking of intructor baed on their coure experience are quite imilar to ranking of intructor baed on raw tudent evaluation. Neverthele, we prefer to rank intructor baed on the coure experience both becaue it control for difference in grading leniency and becaue it eliminate the incentive to inflate grade. Second, ranking of intructor baed on learning are markedly different from ranking of intructor baed on tudent evaluation. The optimal aement of intructor quality would not be baed on raw tudent evaluation but rather on a weighted average of the coure experience and learning variable, where the weight correpond to the relative importance that ociety aign to thee two objective. The optimal aement of intructor quality i eaily implemented. It applie baic regreion to data that are available in machine-readable form at mot or all intitution. Four point are worth highlighting. Firt, contructing optimal evaluation require one to elect the proper weight to be placed on learning relative to the coure experience. Second, in order to 23

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