A Novel Teacher Evaluation Model for Faculty Development

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Branching Paths: A Novel Teacher Evaluation Model for Faculty Development

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James P. Bavis and Ahn G. Nu

Department of English, Purdue University

ENGL 101: First Year Writing

Dr. Richard Teeth  

January 30, 2020

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Branching Paths: A Novel Teacher Evaluation Model for Faculty Development

According to Theall (2017), “Faculty evaluation and development cannot be considered  

separately… evaluation without development is punitive, and development without evaluation is  

guesswork” (p.91). As the practices that constitute modern programmatic faculty development  

have evolved from their humble beginnings to become a commonplace feature of university life  

(Lewis, 1996), a variety of tactics to evaluate the proficiency of teaching faculty for development  

purposes have likewise become commonplace. These include measures as diverse as peer  

observations, the development of teaching portfolios, and student evaluations.  

One such measure, the student evaluation of teacher (SET), has been virtually ubiquitous  

since at least the 1990s (Wilson, 1998). Though records of SET-like instruments can be traced to  

work at Purdue University in the 1920s (Remmers & Brandenburg, 1927), most modern histories  

of faculty development suggest that their rise to widespread popularity went hand-in-hand with  

the birth of modern faculty development programs in the 1970s, when universities began to  

adopt them in response to student protest movements criticizing mainstream university curricula  

and approaches to instruction (Gaff & Simpson, 1994; Lewis, 1996; McKeachie, 1996). By the  

mid-2000s, researchers had begun to characterize SETs in terms like “…the predominant measure  

of university teacher performance […] worldwide” (Pounder, 2007, p. 178). Today, SETs play an  

important role in teacher assessment and faculty development at most universities (Davis, 2009).  

Recent SET research practically takes the presence of some form of this assessment on most  

campuses as a given. Spooren et al. (2017), for instance, merely note that that SETs can be found  

at “almost every institution of higher education throughout the world” (p. 130). Similarly,  

Darwin (2012) refers to teacher evaluation as an established orthodoxy, labeling it a “venerated,”  

“axiomatic” institutional practice (p. 733).  

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Moreover, SETs do not only help universities direct their faculty development efforts.  

They have also come to occupy a place of considerable institutional importance for their role in  

personnel considerations, informing important decisions like hiring, firing, tenure, and  

promotion. Seldin (1993, as cited in Pounder, 2007) finds that 86% of higher educational  

institutions use SETs as important factors in personnel decisions. A 1991 survey of department  

chairs found 97% used student evaluations to assess teaching performance (US Department of  

Education). Since the mid-late 1990s, a general trend towards comprehensive methods of teacher  

evaluation that include multiple forms of assessment has been observed (Berk, 2005). However,  

recent research suggests the usage of SETs in personnel decisions is still overwhelmingly  

common, though hard percentages are hard to come by, perhaps owing to the multifaceted nature  

of these decisions (Boring et al., 2017; Galbraith et al., 2012). In certain contexts, student  

evaluations can also have ramifications beyond the level of individual instructors. Particularly as  

public schools have experienced pressure in recent decades to adopt neoliberal, market-based  

approaches to self-assessment and adopt a student-as-consumer mindset (Darwin, 2012;  

Marginson, 2009), information from evaluations can even feature in department- or school-wide  

funding decisions (see, for instance, the Obama Administration’s Race to the Top initiative,  

which awarded grants to K-12 institutions that adopted value-added models for teacher  

evaluation).  

However, while SETs play a crucial role in faulty development and personnel decisions  

for many education institutions, current approaches to SET administration are not as well-suited  

to these purposes as they could be. This paper argues that a formative, empirical approach to  

teacher evaluation developed in response to the demands of the local context is better-suited for  

helping institutions improve their teachers. It proposes the Heavilon Evaluation of Teacher, or  

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HET, a new teacher assessment instrument that can strengthen current approaches to faculty  

development by making them more responsive to teachers’ local contexts. It also proposes a pilot  

study that will clarify the differences between this new instrument and the Introductory  

Composition at Purdue (ICaP) SET, a more traditional instrument used for similar purposes. The  

results of this study will direct future efforts to refine the proposed instrument. Methods section,  

which follows, will propose a pilot study that compares the results of the proposed instrument to  

the results of a traditional SET (and will also provide necessary background information on both  

of these evaluations). The paper will conclude with a discussion of how the results of the pilot

study will inform future iterations of the proposed instrument and, more broadly, how  

universities should argue for local development of assessments.  

Literature Review

Effective Teaching: A Contextual Construct

The validity of the instrument this paper proposes is contingent on the idea that it is  

possible to systematically measure a teacher’s ability to teach. Indeed, the same could be said for  

virtually all teacher evaluations. Yet despite the exceeding commonness of SETs and the faculty  

development programs that depend on their input, there is little scholarly consensus on precisely  

what constitutes “good” or “effective” teaching. It would be impossible to review the entire  

history of the debate surrounding teaching effectiveness, owing to its sheer scope—such a  

summary might need to begin with, for instance, Cicero and Quintilian. However, a cursory  

overview of important recent developments (particularly those revealed in meta-analyses of  

empirical studies of teaching) can help situate the instrument this paper proposes in relevant  

academic conversations.  

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Meta-analysis 1  

One core assumption that undergirds many of these conversations is the notion that good  

teaching has effects that can be observed in terms of student achievement. A meta-analysis of  

167 empirical studies that investigated the effects of various teaching factors on student  

achievement (Kyriakides et al., 2013) supported the effectiveness of a set of teaching factors that  

the authors group together under the label of the “dynamic model” of teaching. Seven of the  

eight factors (Orientation, Structuring, Modeling, Questioning, Assessment, Time Management,  

and Classroom as Learning Environment) corresponded to moderate average effect sizes (of  

between 0.34–0.41 standard deviations) in measures of student achievement. The eighth factor,  

Application (defined as seatwork and small-group tasks oriented toward practice of course  

concepts), corresponded to only a small yet still significant effect size of 0.18. The lack of any  

single decisive factor in the meta-analysis supports the idea that effective teaching is likely a  

multivariate construct. However, the authors also note the context-dependent nature of effective  

teaching. Application, the least-important teaching factor overall, proved more important in  

studies examining young students (p. 148). Modeling, by contrast, was especially important for  

older students.  

Meta-analysis 2  

A different meta-analysis that argues for the importance of factors like clarity and setting  

challenging goals (Hattie, 2009) nevertheless also finds that the effect sizes of various teaching  

factors can be highly context-dependent. For example, effect sizes for homework range from  

0.15 (a small effect) to 0.64 (a moderately large effect) based on the level of education examined.  

Similar ranges are observed for differences in academic subject (e.g., math vs. English) and  

student ability level. As Snook et al. (2009) note in their critical response to Hattie, while it is  

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possible to produce a figure for the average effect size of a particular teaching factor, such  

averages obscure the importance of context.  

Meta-analysis 3  

A final meta-analysis (Seidel & Shavelson, 2007) found generally small average effect  

sizes for most teaching factors—organization and academic domain- specific learning activities  

showed the biggest cognitive effects (0.33 and 0.25, respectively). Here, again, however,  

effectiveness varied considerably due to contextual factors like domain of study and level of  

education in ways that average effect sizes do not indicate.  

These pieces of evidence suggest that there are multiple teaching factors that produce  

measurable gains in student achievement and that the relative importance of individual factors  

can be highly dependent on contextual factors like student identity. This is in line with a welldocumented phenomenon in educational research that complicates attempts to measure teaching  

effectiveness purely in terms of student achievement. This is that “the largest source of variation  

in student learning is attributable to differences in what students bring to school – their abilities  

and attitudes, and family and community” (McKenzie et al., 2005, p. 2). Student achievement  

varies greatly due to non-teacher factors like socio-economic status and home life (Snook et al.,  

2009). This means that, even to the extent that it is possible to observe the effectiveness of  

certain teaching behaviors in terms of student achievement, it is difficult to set generalizable  

benchmarks or standards for student achievement. Thus is it also difficult to make true apples-toapples comparisons about teaching effectiveness between different educational contexts: due to  

vast differences between different kinds of students, a notion of what constitutes highly effective  

teaching in one context may not in another. This difficulty has featured in criticism of certain  

meta-analyses that have purported to make generalizable claims about what teaching factors  

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produce the biggest effects (Hattie, 2009). A variety of other commentators have also made  

similar claims about the importance of contextual factors in teaching effectiveness for decades  

(see, e.g., Bloom et al., 1956; Cashin, 1990; Theall, 2017).  

The studies described above mainly measure teaching effectiveness in terms of academic  

achievement. It should certainly be noted that these quantifiable measures are not generally  

regarded as the only outcomes of effective teaching worth pursuing. Qualitative outcomes like  

increased affinity for learning and greater sense of self-efficacy are also important learning goals.  

Here, also, local context plays a large role.

SETs: Imperfect Measures of Teaching  

As noted in this paper’s introduction, SETs are commonly used to assess teaching  

performance and inform faculty development efforts. Typically, these take the form of an end-ofterm summative evaluation comprised of multiple-choice questions (MCQs) that allow students  

to rate statements about their teachers on Likert scales. These are often accompanied with shortanswer responses which may or may not be optional.  

SETs serve important institutional purposes. While commentators have noted that there  

are crucial aspects of instruction that students are not equipped to judge (Benton & Young,  

2018), SETs nevertheless give students a rare institutional voice. They represent an opportunity  

to offer anonymous feedback on their teaching experience and potentially address what they  

deem to be their teacher’s successes or failures. Students are also uniquely positioned to offer  

meaningful feedback on an instructors’ teaching because they typically have much more  

extensive firsthand experience of it than any other educational stakeholder. Even peer observers  

only witness a small fraction of the instructional sessions during a given semester. Students with  

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perfect attendance, by contrast, witness all of them. Thus, in a certain sense, a student can  

theoretically assess a teacher’s ability more authoritatively than even peer mentors can.  

While historical attempts to validate SETs have produced mixed results, some studies  

have demonstrated their promise. Howard (1985), for instance, finds that SET are significantly  

more predictive of teaching effectiveness than self-report, peer, and trained-observer  

assessments. A review of several decades of literature on teaching evaluations (Watchel, 1998)  

found that a majority of researchers believe SETs to be generally valid and reliable, despite  

occasional misgivings. This review notes that even scholars who support SETs frequently argue  

that they alone cannot direct efforts to improve teaching and that multiple avenues of feedback  

are necessary (L’hommedieu et al., 1990; Seldin, 1993).  

Finally, SETs also serve purposes secondary to the ostensible goal of improving  

instruction that nonetheless matter. They can be used to bolster faculty CVs and assign  

departmental awards, for instance. SETs can also provide valuable information unrelated to  

teaching. It would be hard to argue that it not is useful for a teacher to learn, for example, that a  

student finds the class unbearably boring, or that a student finds the teacher’s personality so  

unpleasant as to hinder her learning. In short, there is real value in understanding students’  

affective experience of a particular class, even in cases when that value does not necessarily lend  

itself to firm conclusions about the teacher’s professional abilities.  

However, a wealth of scholarly research has demonstrated that SETs are prone to fail in  

certain contexts. A common criticism is that SETs can frequently be confounded by factors  

external to the teaching construct. The best introduction to the research that serves as the basis  

for this claim is probably Neath (1996), who performs something of a meta-analysis by  

presenting these external confounds in the form of twenty sarcastic suggestions to teaching  

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faculty. Among these are the instructions to “grade leniently,” “administer ratings before tests”  

(p. 1365), and “not teach required courses” (#11) (p. 1367). Most of Neath’s advice reflects an  

overriding observation that teaching evaluations tend to document students’ affective feelings  

toward a class, rather than their teachers’ abilities, even when the evaluations explicitly ask  

students to judge the latter.  

Beyond Neath, much of the available research paints a similar picture. For example, a  

study of over 30,000 economics students concluded that “the poorer the student considered his  

teacher to be [on an SET], the more economics he understood” (Attiyeh & Lumsden, 1972). A  

1998 meta-analysis argued that “there is no evidence that the use of teacher ratings improves  

learning in the long run” (Armstrong, 1998, p. 1223). A 2010 National Bureau of Economic  

Research study found that high SET scores for a course’s instructor correlated with “high  

contemporaneous course achievement,” but “low follow-on achievement” (in other words, the  

students would tend to do well in the course, but poor in future courses in the same field of study.  

Others observing this effect have suggested SETs reward a pandering, “soft-ball” teaching style  

in the initial course (Carrell & West, 2010). More recent research suggests that course topic can  

have a significant effect on SET scores as well: teachers of “quantitative courses” (i.e., mathfocused classes) tend to receive lower evaluations from students than their humanities peers (Uttl  

& Smibert, 2017).  

Several modern SET studies have also demonstrated bias on the basis of gender  

(Anderson & Miller, 1997; Basow, 1995), physical appearance/sexiness (Ambady & Rosenthal,  

1993), and other identity markers that do not affect teaching quality. Gender, in particular, has  

attracted significant attention. One recent study examined two online classes: one in which  

instructors identified themselves to students as male, and another in which they identified as  

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female (regardless of the instructor’s actual gender) (Macnell et al., 2015). The classes were

identical in structure and content, and the instructors’ true identities were concealed from  

students. The study found that students rated the male identity higher on average. However, a  

few studies have demonstrated the reverse of the gender bias mentioned above (that is, women  

received higher scores) (Bachen et al., 1999) while others have registered no gender bias one  

way or another (Centra & Gaubatz, 2000).  

The goal of presenting these criticisms is not necessarily to diminish the institutional  

importance of SETs. Of course, insofar as institutions value the instruction of their students, it is  

important that those students have some say in the content and character of that instruction.  

Rather, the goal here is simply to demonstrate that using SETs for faculty development  

purposes—much less for personnel decisions—can present problems. It is also to make the case  

that, despite the abundance of literature on SETs, there is still plenty of room for scholarly  

attempts to make these instruments more useful.

Empirical Scales and Locally-Relevant Evaluation  

One way to ensure that teaching assessments are more responsive to the demands of  

teachers’ local contexts is to develop those assessments locally, ideally via a process that  

involves the input of a variety of local stakeholders. Here, writing assessment literature offers a  

promising path forward: empirical scale development, the process of structuring and calibrating  

instruments in response to local input and data (e.g., in the context of writing assessment, student  

writing samples and performance information). This practice contrasts, for instance, with  

deductive approaches to scale development that attempt to represent predetermined theoretical  

constructs so that results can be generalized.  

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Supporters of the empirical process argue that empirical scales have several advantages.  

They are frequently posited as potential solutions to well-documented reliability and validity  

issues that can occur with theoretical or intuitive scale development (Brindley, 1998; Turner &  

Upshur, 1995, 2002). Empirical scales can also help researchers avoid issues caused by  

subjective or vaguely-worded standards in other kinds of scales (Brindley, 1998) because they  

require buy-in from local stakeholders who must agree on these standards based on their  

understanding of the local context. Fulcher et al. (2011) note the following, for instance:  

Measurement-driven scales suffer from descriptional inadequacy. They are not sensitive  

to the communicative context or the interactional complexities of language use. The level  

of abstraction is too great, creating a gulf between the score and its meaning. Only with a  

richer description of contextually based performance, can we strengthen the meaning of  

the score, and hence the validity of score-based inferences. (pp. 8–9)  

There is also some evidence that the branching structure of the EBB scale specifically can  

allow for more reliable and valid assessments, even if it is typically easier to calibrate and use  

conventional scales (Hirai & Koizumi, 2013). Finally, scholars have also argued that theorybased approaches to scale development do not always result in instruments that realistically  

capture ordinary classroom situations (Knoch, 2007, 2009).  

[Original paragraph removed for brevity.]

Materials and Methods  

This section proposes a pilot study that will compare the ICaP SET to the Heavilon  

Evaluation of Teacher (HET), an instrument designed to combat the statistical ceiling effect  

described above. In this section, the format and composition of the HET is described, with  

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special attention paid to its branching scale design. Following this, the procedure for the study is  

outlined, and planned interpretations of the data are discussed.  

The Purdue ICaP SET  

The SET employed by Introductory Composition at Purdue (ICaP) program as of January  

2019 serves as an example of many of the prevailing trends in current SET administration.  

[Original two paragraphs removed for brevity.]

The remainder of the MCQs (thirty in total) are chosen from a list of 646 possible  

questions provided by the Purdue Instructor Course Evaluation Service (PICES) by department  

administrators. Each of these PICES questions requires students to respond to a statement about  

the course on a five-point Likert scale. Likert scales are simple scales used to indicate degrees of  

agreement. In the case of the ICaP SET, students must indicate whether they strongly agree,  

agree, disagree, strongly disagree, or are undecided. These thirty Likert scale questions assess a  

wide variety of the course and instructor’s qualities. Examples include “My instructor seems  

well-prepared for class,” “This course helps me analyze my own and other students’ writing,”  

and “When I have a question or comment I know it will be respected,” for example.  

[Original paragraph removed for brevity.]

Insofar as it is distributed digitally, it is composed of MCQs (plus a few short-answer  

responses), and it is intended as end-of-term summative assessment, the ICaP SET embodies he  

current prevailing trends in university-level SET administration. In this pilot study, it serves as a  

stand-in for current SET administration practices (as generally conceived).

The HET

Like the ICaP SET, the HET uses student responses to questions to produce a score that  

purports to represent their teacher’s pedagogical ability. It has a similar number of items (28, as  

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opposed to the ICaP SET’s 34). However, despite these superficial similarities, the instrument’s  

structure and content differ substantially from the ICaP SET’s.  

The most notable differences are the construction of the items on the text and the way  

that responses to these items determine the teacher’s final score. Items on the HET do not use the  

typical Likert scale, but instead prompt students to respond to a question with a simple “yes/no”  

binary choice. By answering “yes” and “no” to these questions, student responders navigate a  

branching “tree” map of possibilities whose endpoints correspond to points on a 33- point ordinal  

scale.  

The items on the HET are grouped into six suites according to their relevance to six  

different aspects of the teaching construct (described below). The suites of questions correspond  

to directional nodes on the scale—branching paths where an instructor can move either “up” or  

“down” based on the student’s responses. If a student awards a set number of “yes” responses to  

questions in a given suite (signifying a positive perception of the instructor’s teaching), the  

instructor moves up on the scale. If a student does not award enough “yes” responses, the  

instructor moves down. Thus, after the student has answered all of the questions, the instructor’s  

“end position” on the branching tree of possibilities corresponds to a point on the 33-point scale.  

A visualization of this structure is presented in Figure 1.  

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Figure 1

Illustration of HET’s Branching Structure  

Note. Each node in this diagram corresponds to a suite of HET/ICALT items, rather than to a  

single item.

The questions on the HET derive from the International Comparative Analysis of  

Learning and Teaching (ICALT), an instrument that measures observable teaching behaviors for  

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the purpose of international pedagogical research within the European Union. The most recent  

version of the ICALT contains 32 items across six topic domains that correspond to six broad  

teaching skills. For each item, students rate a statement about the teacher on a four-point Likert  

scale. The main advantage of using ICALT items in the HET is that they have been  

independently tested for reliability and validity numerous times over 17 years of development  

(see, e.g., Van de Grift, 2007). Thus, their results lend themselves to meaningful comparisons  

between teachers (as well as providing administrators a reasonable level of confidence in their  

ability to model the teaching construct itself). The six “suites” of questions on the HET, which  

correspond to the six topic domains on the ICALT, are presented in Table 1.  

Table 1

HET Question Suites  

Suite Description No. of items

Safe learning  

environment

Whether the teacher is able to maintain positive,  

nonthreatening relationships with students (and  

to foster these sorts of relationships among

students).

4

Classroom  

management

Whether the teacher is able to maintain an orderly,  

predictable environment.

4

Clear instruction Whether the teacher is able to explain class topics  

comprehensibly, set clear goals, and connect  

assignments and outcomes in helpful ways.

7

Activating teaching  

methods

Whether the teacher uses strategies that motivate  

students to think about the class’s topics.  

7

Learning strategies Whether teachers take explicit steps to teach  

students how to learn (as opposed to merely  

providing students informational content).  

6

Differentiation Whether teachers can successfully adjust their  

behavior to meet the diverse needs of individual  

students.  

4

Note. Item numbers are derived from original ICALT item suites.

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The items on the HET are modified from the ICALT items only insofar as they are  

phrased as binary choices, rather than as invitations to rate the teacher. Usually, this means the  

addition of the word “does” and a question mark at the end of the sentence. For example, the  

second safe learning climate item on the ICALT is presented as “The teacher maintains a relaxed  

atmosphere.” On the HET, this item is rephrased as, “Does the teacher maintain a relaxed  

atmosphere?” See Appendix for additional sample items.

As will be discussed below, the ordering of item suits plays a decisive role in the  

teacher’s final score because the branching scale rates earlier suites more powerfully. So too does  

the “sensitivity” of each suite of items (i.e., the number of positive responses required to progress  

upward at each branching node). This means that it is important for local stakeholders to  

participate in the development of the scale. In other words, these stakeholders must be involved  

in decisions about how to order the item suites and adjust the sensitivity of each node. This is  

described in more detail below.  

Once the scale has been developed, the assessment has been administered, and the  

teacher’s endpoint score has been obtained, the student rater is prompted to offer any textual  

feedback that they feel summarizes the course experience, good or bad. Like the short response  

items in the ICaP SET, this item is optional. The short-response item is as follows:

• What would you say about this instructor, good or bad, to another student considering  

taking this course?

The final four items are demographic questions. For these, students indicate their grade level,  

their expected grade for the course, their school/college (e.g., College of Liberal Arts, School of  

Agriculture, etc.), and whether they are taking the course as an elective or as a degree

Commented [AF28]: In addition to presenting figures and  

tables in the text, you may also present them in appendices at  

the end of the document.

You may also use appendices to present material that would  

be distracting or tedious in the body of the paper. In either  

case, you can use simple in-text references to direct readers  

to the appendices. If you have multiple appendices, you  

would reference in the text “Appendix A,” “Appendix B,”

and so on. This paper only has one appendix, so it is simply  

labeled Appendix.

Commented [AF29]: For the sake of brevity, the rest of  

the body of the paper has been omitted.

17

References

Ambady, N., & Rosenthal, R. (1993). Half a minute: Predicting teacher evaluations from thin  

slices of nonverbal behavior and physical attractiveness. Journal of Personality and  

Social Psychology, 64(3), 431–441. http://dx.doi.org/10.1037/0022-3514.64.3.431

American Association of University Professors. (n.d.). Background facts on contingent faculty  

positions. https://www.aaup.org/issues/contingency/background-facts

American Association of University Professors. (2018, October 11). Data snapshot: Contingent  

faculty in US higher ed. AAUP Updates. https://www.aaup.org/news/data-snapshotcontingent-faculty-us-higher-ed#.Xfpdmy2ZNR4

Anderson, K., & Miller, E. D. (1997). Gender and student evaluations of teaching. PS: Political

Science and Politics, 30(2), 216–219. https://doi.org/10.2307/420499

Armstrong, J. S. (1998). Are student ratings of instruction useful? American Psychologist,  

53(11), 1223–1224. http://dx.doi.org/10.1037/0003-066X.53.11.1223

Attiyeh, R., & Lumsden, K. G. (1972). Some modern myths in teaching economics: The U.K.  

experience. American Economic Review, 62(1), 429–443.  

https://www.jstor.org/stable/1821578

Bachen, C. M., McLoughlin, M. M., & Garcia, S. S. (1999). Assessing the role of gender in  

college students’ evaluations of faculty. Communication Education, 48(3), 193–210.  

http://doi.org/cqcgsr

Basow, S. A. (1995). Student evaluations of college professors: When gender matters. Journal of  

Educational Psychology, 87(4), 656–665. http://dx.doi.org/10.1037/0022-0663.87.4.656

Becker, W. (2000). Teaching economics in the 21st century. Journal of Economic Perspectives,  

14(1), 109–120. http://dx.doi.org/10.1257/jep.14.1.109

Commented [AF30]: Start the references list on a new  

page. The word “References” (or “Reference,” if there is only  

one source), should appear bolded and centered at the top of  

the page. Reference entries should follow in alphabetical  

order. There should be a reference entry for every source

cited in the text.

All citation entries should be double-spaced. After the first  

line of each entry, every following line should be indented a  

half inch (this is called a “hanging indent”). Most word  

processors do this automatically via a formatting menu; do

not use tabs for a hanging indent unless your program  

absolutely will not create a hanging indent for you.

Commented [AWC31]: Source with two authors.

Field Code Changed

Commented [AWC32]: Source with organizational  

author.

Field Code Changed

Field Code Changed

Field Code Changed

Field Code Changed

Commented [AWC33]: Note that sources in online  

academic publications like scholarly journals now require  

DOIs or stable URLs if they are available.

Field Code Changed

18

Benton, S., & Young, S. (2018). Best practices in the evaluation of teaching. Idea paper, 69.

Berk, R. A. (2005). Survey of 12 strategies to measure teaching effectiveness. International  

Journal of Teaching and Learning in Higher Education, 17(1), 48–62.

Bloom, B. S., Englehart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1956). Taxonomy  

of educational objectives: The classification of educational goals. Addison-Wesley  

Longman Ltd.

Carrell, S., & West, J. (2010). Does professor quality matter? Evidence from random assignment  

of students to professors. Journal of Political Economy, 118(3), 409–432.  

https://doi.org/10.1086/653808

Cashin, W. E. (1990). Students do rate different academic fields differently. In M. Theall & J. L.  

Franklin (Eds.), Student ratings of instruction: Issues for improving practice (pp. 113–

121).

Centra, J., & Gaubatz, N. (2000). Is there gender bias in student evaluations of teaching? The

Journal of Higher Education, 71(1), 17–33.

https://doi.org/10.1080/00221546.2000.11780814

Davis, B. G. (2009). Tools for teaching (2nd ed.). Jossey-Bass.

Denton, D. (2013). Responding to edTPA: Transforming practice or applying shortcuts?  

AILACTE Journal, 10(1), 19–36.

[For the sake of brevity, the rest of the references have been omitted.]

Commented [AWC34]: Example of a book in print.

Commented [AWC35]: Chapter in an edited collection.

Field Code Changed

Commented [AWC36]: Academic article for which a DOI  

was unavailable.

19

Appendix

Sample ICALT Items Rephrased for HET

Suite Sample ICALT item HET phrasing

Safe learning  

environment

The teacher promotes mutual  

respect.

Does the teacher promote  

mutual respect?

Classroom  

management

The teacher uses learning time  

efficiently.

Does the teacher use learning  

time efficiently?

Clear instruction The teacher gives feedback to  

pupils.

Does the teacher give feedback  

to pupils?

Activating teaching  

methods

The teacher provides interactive  

instruction and activities.

Does the teacher provide  

interactive instruction and  

activities?

Learning strategies The teacher uses multiple  

learning strategies.

Does the teacher use multiple  

learning strategies?

Differentiation The teacher adapts the  

instruction to the relevant  

differences between pupils.

Does the teacher adapt the  

instruction to the relevant  

differences between pupils?

Commented [AF37]: Appendices begin after the  

references list. The word “Appendix” should appear at the  

top of the page, bolded and centered. If there are multiple  

appendices, label them with capital letters (e.g., Appendix A,  

Appendix B, and Appendix C). Start each appendix on a new  

page.

Paragraphs of text can also appear in appendices. If they do,  

paragraphs should be indented normally, as they are in the  

body of the paper.

If an appendix contains only a single table or figure, as this  

one does, the centered and bolded “Appendix” replaces the  

centered and bolded label that normally accompanies a table  

or figure.

If the appendix contains both text and tables or figures, the  

tables or figures should be labeled, and these labels should  

include the letter of the appendix in the label. For example, if  

Appendix A contains two tables and one figure, they should  

be labeled “Table A1,” “Table A2,” and “Figure A1.” A table  

that follows in Appendix B should be labeled “Table B1.” If  

there is only one appendix, use the letter “A” in table/figure  

labels: “Table A1,” “Table A2,” and so on.


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