The teacher uses multiple learning strategies

Branching Paths: A Novel Teacher Evaluation Model for Faculty Development
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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sometimes it isn’t practical (too large of a page range, for
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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
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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referenced in the second-listed one.
Include an entry in the reference list only for the secondary
source (Pounder, in this case).
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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
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
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.
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).
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
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.
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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more information on tables and figures, see our resource on
the OWL.
Table notes are optional.
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
Whether the teacher is able to maintain positive,
nonthreatening relationships with students (and
to foster these sorts of relationships among
Whether the teacher is able to maintain an orderly,
predictable environment.
Clear instruction Whether the teacher is able to explain class topics
comprehensibly, set clear goals, and connect
assignments and outcomes in helpful ways.
Activating teaching
Whether the teacher uses strategies that motivate
students to think about the class’s topics.
Learning strategies Whether teachers take explicit steps to teach
students how to learn (as opposed to merely
providing students informational content).
Differentiation Whether teachers can successfully adjust their
behavior to meet the diverse needs of individual
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
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You may also use appendices to present material that would
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and so on. This paper only has one appendix, so it is simply
labeled Appendix.
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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.
American Association of University Professors. (n.d.). Background facts on contingent faculty
American Association of University Professors. (2018, October 11). Data snapshot: Contingent
faculty in US higher ed. AAUP Updates.
Anderson, K., & Miller, E. D. (1997). Gender and student evaluations of teaching. PS: Political
Science and Politics, 30(2), 216–219.
Armstrong, J. S. (1998). Are student ratings of instruction useful? American Psychologist,
53(11), 1223–1224.
Attiyeh, R., & Lumsden, K. G. (1972). Some modern myths in teaching economics: The U.K.
experience. American Economic Review, 62(1), 429–443.
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.
Basow, S. A. (1995). Student evaluations of college professors: When gender matters. Journal of
Educational Psychology, 87(4), 656–665.
Becker, W. (2000). Teaching economics in the 21st century. Journal of Economic Perspectives,
14(1), 109–120.
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
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
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.
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–
Centra, J., & Gaubatz, N. (2000). Is there gender bias in student evaluations of teaching? The
Journal of Higher Education, 71(1), 17–33.
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.
Sample ICALT Items Rephrased for HET
Suite Sample ICALT item HET phrasing
Safe learning
The teacher promotes mutual
Does the teacher promote
mutual respect?
The teacher uses learning time
Does the teacher use learning
time efficiently?
Clear instruction The teacher gives feedback to
Does the teacher give feedback
to pupils?
Activating teaching
The teacher provides interactive
instruction and activities.
Does the teacher provide
interactive instruction and
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
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.
Filename: APA 7 Student Sample Paper.docx
y/Application Support/Microsoft/Office/16.0/DTS/en-US{C81676A6-F081-DC4F9C53-30B6590914A3}/{8EBDE819-F094-B34F-8FBC98F15DA9B8B0}tf10002091.dotx
Title: Branching Paths: A Novel Teacher Evaluation Model for Faculty
Author: Victoria Ruiz
Creation Date: 10/19/20 3:01:00 PM
Change Number: 4
Last Saved On: 10/19/20 3:10:00 PM
Last Saved By: APA Formatting
Total Editing Time: 6 Minutes
Last Printed On: 10/19/20 3:10:00 PM
As of Last Complete Printing
Number of Pages: 19
Number of Words:4,713 (approx.)
Number of Characters: 26,868 (approx.)
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