Validation of Automated Scores of TOEFL iBT Tasks Against Nontest

Validation of Automated Scores of

TOEFL iBT

Tasks Against Nontest

Indicators of Writing Ability

Sara Cushing Weigle

June 2011

TOEFL iBT

Research Report

TOEFL iBT–15

Validation of Automated Scores of TOEFL iBT

Tasks

Against Nontest Indicators of Writing Ability

Sara Cushing Weigle

Georgia State University, Atlanta

RR-11-24

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Abstract

Automated scoring has the potential to dramatically reduce the time and costs associated with the

assessment of complex skills such as writing, but its use must be validated against a variety of

criteria for it to be accepted by test users and stakeholders. This study addresses two validity-

related issues regarding the use of e-rater

with the independent writing task on the TOEFL

iBT

(Internet-based test). First, relationships between automated scores of iBT tasks and nontest

indicators of writing ability were examined. This was followed by exploration of prompt-related

differences in automated scores of essays written by the same examinees. Correlations between

both human and e-rater scores and nontest indicators were moderate but consistent, with few

differences between e-rater and human rater scores. E-rater was more consistent across prompts

than individual human raters, although there were differences in scores across prompts for the

individual features used to generate total e-rater scores.

Key words: automated scoring, writing assessment, second language, validity, e-rater

The TOEFL

exam was developed in 1963 by the National Council on the Testing of English as a

Foreign Language. The Council was formed through the cooperative effort of more than 30 public and

private organizations concerned with testing the English proficiency of nonnative speakers of the

language applying for admission to institutions in the United States. In 1965, Educational Testing

Service (ETS) and the College Board

assumed joint responsibility for the program. In 1973, a

cooperative arrangement for the operation of the program was entered into by ETS, the College Board,

and the Graduate Record Examinations

(GRE

) Board. The membership of the College Board is

composed of schools, colleges, school systems, and educational associations; GRE Board members are

associated with graduate education. The test is now wholly owned and operated by ETS.

ETS administers the TOEFL program under the general direction of a policy board that was

established by, and is affiliated with, the sponsoring organizations. Members of the TOEFL Board

(previously the Policy Council) represent the College Board, the GRE Board, and such institutions and

agencies as graduate schools of business, two-year colleges, and nonprofit educational exchange

agencies.

  

Since its inception in 1963, the TOEFL has evolved from a paper-based test to a computer-based test

and, in 2005, to an Internet-based test, TOEFL iBT

. One constant throughout this evolution has been

a continuing program of research related to the TOEFL test. From 1977 to 2005, nearly 100 research

and technical reports on the early versions of TOEFL were published. In 1997, a monograph series that

laid the groundwork for the development of TOEFL iBT was launched. With the release of TOEFL

iBT, a TOEFL iBT report series has been introduced.

Currently this research is carried out in consultation with the TOEFL Committee of Examiners. Its

members include representatives of the TOEFL Board and distinguished English as a second language

specialists from the academic community. The Committee advises the TOEFL program about research

needs and, through the research subcommittee, solicits, reviews, and approves proposals for funding

and reports for publication. Members of the Committee of Examiners serve four-year terms at the

invitation of the Board; the chair of the committee serves on the Board.

Current (2010-2011) members of the TOEFL Committee of Examiners are:

Alister Cumming (Chair) University of Toronto

Carol A. Chapelle Iowa State University

Barbara Hoekje Drexel University

Ari Huhta University of Jyväskylä, Finland

John M. Norris University of Hawaii at Manoa

James Purpura Columbia University

Carsten Roever University of Melbourne

Steve Ross University of Maryland

Mikyuki Sasaki Nagoya Gakuin University

Norbert Schmitt University of Nottingham

Robert Schoonen University of Amsterdam

Ling Shi University of British Columbia

To obtain more information about the TOEFL programs and services, use one of the following:

E-mail: toefl@ets.org

Web site:

www.ets.org/toefl

iii

Acknowledgments

The study was funded by the TOEFL

Committee of Examiners and the TOEFL program at

ETS. Additional research support came from the Department of Applied Linguistics & ESL at

Georgia State University. Yanbin Lu and Amanda Baker assisted with data collection and

analysis, and Liang Guo helped with the preparation of the final report. Patricia Carrell served as

consultant to the project, primarily assisting with instrument design and planning data collection.

ETS provided the essay prompts for the study, scored all the essays with e-rater

, and arranged

for human raters for about half of the essays. I would like to thank the site coordinators at the

institutions where I collected data: Robert Nelson, Lily Compton, Kristin Di Gennaro, Cameron

Jaynes, Jennifer Lund, Megan Kilbourn, Nur Yigitoglu, Dudley Reynolds, Sunyoung Shin, and

Youngsoon So. Susan Firestone, Joe Lee, Amanda Baker, Holly Joseph, Caroline Payant, Jason

Litzenberg, and Magdi Kandil served as raters for the TOEFL iBT

essays and the submitted

writing samples. Finally I would like to thank the reviewers at ETS, and in particular Mary

Enright, for their helpful comments on earlier drafts of this report. The responsibility for any

remaining errors is solely mine, and the ideas and opinions expressed in the report are those of

the author, not necessarily of ETS or the TOEFL program.

Table of Contents

Page

Literature Review............................................................................................................................ 2

Validity of Automated Scoring Systems ................................................................................. 3

Automated Scoring and Nonnative Writing ............................................................................ 6

Task Variability in Writing Assessment .................................................................................. 8

Method .......................................................................................................................................... 10

Research Questions ................................................................................................................ 10

Participants ............................................................................................................................ 10

Materials ................................................................................................................................ 12

Procedures .............................................................................................................................. 14

Research Question 1: Results and Discussion .............................................................................. 18

Results.................................................................................................................................... 18

Discussion .............................................................................................................................. 29

Research Question 2: Results and Discussion .............................................................................. 30

Results.................................................................................................................................... 30

Discussion .............................................................................................................................. 32

Research Question 3: Results and Discussion .............................................................................. 33

Results.................................................................................................................................... 33

Discussion .............................................................................................................................. 36

Implications and Future Directions ............................................................................................... 38

References ..................................................................................................................................... 41

Notes ............................................................................................................................................. 46

List of Appendices ........................................................................................................................ 47

List of Tables

Page

Table 1 Participant Characteristics ............................................................................................ 11

Table 2 Interrater Reliability of e-rater and Human Rater Scores ............................................. 15

Table 3 Scoring Rubric for Submitted Writing Samples ........................................................... 16

Table 4 Descriptive Statistics of Global Self-Evaluation Variables ......................................... 19

Table 5 Correlations Among Global Self-Evaluation Variables and Student

Survey Scales ............................................................................................................... 19

Table 6 Average of Correlations Between Scores on TOEFL iBT Tasks and

Self-Evaluation Variables ............................................................................................. 19

Table 7 t-test of Difference in Magnitude of Correlations of e-rater and the

Average of Two Human Raters With Self-Evaluation Variables ................................ 21

Table 8 Descriptive Statistics and Average Correlations Between Scores on TOEFL iBT

Tasks and Student Survey Scales (Higher Mean Scores = Fewer Problems) .............. 21

Table 9 Descriptive Statistics for Instructor Ratings of Overall Performance and

Proficiency .................................................................................................................... 23

Table 10 Correlations Among Instructor Survey Variables ........................................................ 23

Table 11 Average of Correlations Between Scores on TOEFL iBT Tasks and

Instructor Ratings of Overall Performance and Proficiency ........................................ 24

Table 12 Descriptive Statistics for Instructor Survey Scale Variables ........................................ 25

Table 13 Average Correlations of Instructor Survey Scale Variables With e-rater and

Human Ratings of TOEFL iBT Essays ........................................................................ 25

Table 14 Descriptive Statistics and Interrater Agreement Statistics for Submitted Writing

Sample Scores .............................................................................................................. 27

Table 15 Average Correlations of Submitted Writing Sample Scores With TOEFL iBT

Task Scores by Content Area of Writing Samples ....................................................... 27

Table 16 Summary of Highest Average Correlations From Different Data Sources .................. 28

Table 17 Correlations of Averaged e-rater Feature Scores With Self-Evaluation of

Language Skills ............................................................................................................ 30

Table 18 Correlations of Averaged e-rater Feature Scores With Composite Instructor

Evaluation of English Ability ....................................................................................... 31

Table 19 Correlations of Averaged e-rater Feature Scores With Averaged Content

and Language Scores on Submitted Writing Samples ................................................. 31

Table 20 Descriptive Statistics and Paired t-Tests for Individual Raters Across Prompts ......... 33

Table 21 Repeated-Measures ANOVA for Topic and Rater on Scores Using

Individual Raters .......................................................................................................... 34

Table 22 Descriptive Statistics, Correlations, and Results of Paired t-Tests Across

Prompts of e-rater Features (N = 377) .......................................................................... 35

Table 23 Comparison of e-rater Feature Correlations Across Alternate Forms of

Writing Tests ................................................................................................................ 37

Automated scoring has the potential to reduce dramatically the time and resources

associated with the assessment of complex skills such as writing—in particular, the recruitment,

training, and monitoring of raters. Much of the research on automated scoring has compared

automated scores on essays to the scores given by human raters to the same essays, and has

demonstrated convincingly that automated scores are at least as reliable as human scores.

However, the increased use of automated scores for both high- and low-stakes testing has sparked

a great deal of controversy, particularly among writing teachers, who have expressed a variety of

validity-related concerns regarding automated scoring systems. In order for the use of automated

scores to be accepted by test users and other stakeholders, empirical research into the meaning of

automated scores is crucial.

One test for which automated scoring has recently begun to be used is the TOEFL iBT

which is required for admission of nonnative speakers of English to many colleges and

universities in North America. The TOEFL iBT writing section includes two types of writing

tasks: (a) an independent task, in which the test takers are asked to express and support an opinion

on a familiar topic, and (b) an integrated task, in which the test takers are required to demonstrate

an understanding of the relationship between information in a lecture and a reading. Since July

2009 ETS’s automated scoring system e-rater

has been used as one of two raters to score the

independent task (see Enright & Quinlan, 2008, for an evaluation of the use of e-rater with this

task).

Current approaches to the investigation of validity (Kane, 1992; 2001; Mislevy, Steinberg,

& Almond, 2002, 2003) require articulating an interpretive argument for validity by making

explicit the chain of inferences that link a test to its use. These inferences begin with defining the

domain of interest and end with using test scores to make decision. Each inference is then

examined through the collection of evidence that either supports or refutes the inference. Taking

this approach, Chapelle, Enright, and Jamieson (2008) provided a framework for investigating for

the validity of the TOEFL. This framework includes six inferences that must be supported

through empirical studies: domain description, evaluation, generalization, explanation,

extrapolation, and utilization. An accumulation of evidence supporting each of these inferences

thus supports the interpretive argument for TOEFL validity.

This study addresses two validity-related issues regarding the use of e-rater with the

TOEFL iBT: (a) relationships between automated and human scores of TOEFL iBT independent

writing tasks and nontest indicators of writing ability and (b) prompt-related differences in

automated scores of essays written by the same examinees. To situate this study within the

framework of the TOEFL interpretive argument, the research questions address the inferences of

generalization: “observed scores are estimates of expected scores over the relevant parallel

versions of tasks and test forms and across raters" (Chapelle et al., 2008, p. 19) and extrapolation:

“performance on the test is related to other criteria of language proficiency in the academic

context” (Chapelle et al., 2008, p. 21).

Literature Review

Automated scoring of essays has been possible since the 1960s (Page, 1966) but has only

recently been used on a large scale. Several automated scoring systems have been developed,

including the PEG system (Page, 2003); the Intelligent Essay Assessor (Landauer, Laham, &

Foltz, 2003), and IntelliMetric (Elliot, 2003). This study focuses on e-rater, developed by ETS.

For some years e-rater was used operationally, along with human raters, on the Graduate

Management Admissions Test (GMAT), and e-rater is also used in ETS’ online essay evaluation

service, known as Criterion

(Burstein, Chodorow, & Leacock, 2003). This literature review

begins with a description of e-rater, followed by a discussion of validity issues related to

automated scoring in general and then specifically in terms of evaluating the writing of nonnative

speakers of English. The literature review closes with a discussion of the issue of task variability

in writing assessment and finally addresses these issues with regard to the validity argument for

the TOEFL.

E-rater uses a corpus-based approach to analyze essay characteristics, and is trained on a

large set of essays written on a specific prompt to extract a small set of features that are predictive

of scores given by human raters. As described by Chodorow and Burstein (2004), these features

are generally of four types: syntactic, discourse, topical, and lexical. In earlier versions of e-rater,

stepwise linear regression was used to select features for each prompt from the training essays,

and these features were used to predict scores given by human raters in cross-validation studies on

another set of essays on the same prompt.

The current version of e-rater uses a standard set of features across prompts, allowing both

general and prompt-specific modeling for scoring (Attali & Burstein, 2006; Enright & Quinlan,

2010). These features are typically the following, although they may vary for specific analyses:

• Errors in grammar, usage, mechanics, style: These errors are extracted from by the

writing analysis tools used in the Criterion online essay evaluation service (Burstein,

Chodorow, & Leacock, 2003) and are calculated as rates (total number of errors

divided by number of words in the essay).

• Organization and development: e-rater identifies sentences in each essay

corresponding to important parts of an essay (background, thesis, main ideas,

supporting ideas, conclusion). The organization score is based on the number of

discourse elements in the essay. The development variable is the average number of

words of each of these elements in the essay.

• Lexical complexity: e-rater calculates two feature variables related to lexical

characteristics—a measure of the vocabulary level of each word, based on a large

corpus of newspapers and periodicals, and the average word length.

• Prompt-specific vocabulary usage: e-rater compares the vocabulary in each essay with

the vocabulary used in essays at each of the score points on the rating scale and

computes two variables. The first is the score point value, which calculates the score

point to which the essay is most similar, and the second is the cosine correlation value,

which indicates how similar the essay is to essays at the highest point on the rating

scale.

• Essay length: In previous versions of e-rater, essay length was not explicitly included

as a variable, but the current version includes essay length (number of words) so that

its effect can be controlled, particularly in calculating error rates as described above.

Validity of Automated Scoring Systems

Like other commercially available automated scoring systems (e.g., see Elliot, 2003;

Landauer et al., 2003; Page, 2003), e-rater has been demonstrated to be highly correlated with

scores given by human raters (Burstein, 2002; Burstein & Chodorow, 1999). However, despite

findings that automated scores are as reliable as human scores, use of automated scoring has

generated controversy and strong opposition, particularly among composition teachers,

primarily because a computer cannot actually read student writing (Anson, 2006; Herrington &

Moran, 2001).

A recent position statement by the Conference on College Composition and

Communication (CCCC, 2004) stated in part:

The speed of machine-scoring is offset by a number of disadvantages. Writing-to-a-

machine violates the essentially social nature of writing: we write to others for social

purposes. If a student’s first writing-experience at an institution is writing to a machine,

for instance, this sends a message: writing at this institution is not valued as human

communication—and this in turn reduces the validity of the assessment. Further, since we

can not know the criteria by which the computer scores the writing, we can not know

whether particular kinds of bias may have been built into the scoring. And finally, if high

schools see themselves as preparing students for college writing, and if college writing

becomes to any degree machine-scored, high schools will begin to prepare their students

to write for machines. (“A Current Challenge” section, para. 2)

A number of scholars have expressed similar concerns about the consequences of

automated scoring on the teaching and learning of writing. For example, Cheville (2004) noted

that in the real world what counts as an error in one situation may be completely appropriate in

another. The algorithms used in automated scoring have no way of taking into account the

sociolinguistic context in which particular choices of vocabulary or syntax may be seen as errors

or not, and they thereby give students the false idea that errors can be objectively defined and thus

avoided.

Herrington and Moran (2001) argued further that relying on automated scoring systems as

a replacement for on-campus placement programs will result in the loss of staff training that

occurs on campus as faculty develop writing criteria: “So long as placement tests are developed

in-house, there have to be conversations among faculty and administrators about what it means to

be ‘proficient’” (p. 496). Other concerns include the impact of automated scoring on the teaching

and learning of writing (e.g., Cheville, 2004) and the constraints on the assessment task that are

necessary for automated scoring to be feasible (e.g., Condon, 2006; see also Bennett & Bejar,

1998, for a more general discussion of task design considerations for automated scoring).

In terms of the TOEFL interpretive argument (Chapelle et al., 2008), the concerns raised

by these scholars fall under the category of utilization: “The meaning of test scores is clearly

interpretable by admissions officers, test takers, and teachers. The test will have a positive

influence on how English is taught.” (p. 21). Addressing these concerns directly is beyond the

scope of this particular study; however, inferences about utilization of test scores rely on a chain

of evidence for intermediate inferences such as generalization and extrapolation, which are

addressed in this study.

Yang, Buckendahl, Jusziewicz, and Bhola (2002) identified three main approaches to

validating automated scores. One approach involves investigation of the relationship between

automated scores and scores given by human raters. Another approach is to examine relationships

between automated scores and external measures of the same ability (i.e., criterion-related validity

evidence). The third approach is to investigate the scoring process and mental models represented

by automated scoring systems (see for example Attali & Burstein, 2006; Ben-Simon & Bennett,

2007; and Lee, Gentile, & Kantor, 2008 for examples of this line of research). The current study

focuses on the first two of these approaches.

As noted previously, several studies have demonstrated the comparability of scores

between human raters and automated scores (Yang et al.'s first category). One important study in

this area is from Chodorow and Burstein (2004), who found that, once the effects of essay length

were removed, e-rater v. 01 was not sensitive to certain characteristics of writing that human

raters were. Chodorow and Burstein concluded that future improvements to e-rater should be

made to capture some of these characteristics, including additional measures of syntactic

proficiency and word usage. These measures have been included in the current version of e-rater,

as noted above.

The literature in the second category, the criterion-related validity of automated scores, is

scant, although some researchers have looked at the relationship between human scores on

writing assessments and performance on other measures of writing. Breland, Bridgeman, and

Fowles (1999) provided an overview of studies that have investigated the predictive validity of

writing assessments ranging from in-house placement tests to the Law School Admissions Test

(LSAT) and the SAT

Writing Subject Test. The criteria used for these studies have been (a)

course grades, grade point averages, or instructors’ ratings; (b) performance on other writing tasks

(specifically, multiple essays scored by multiple raters); and (c) examinee background indices,

including self-assessment of writing ability. Breland et al. found that essay test performance

correlated more highly with other writing performance than with grades, GPA, or instructors’

ratings.

Studies that have related automated scores to nontest indicators of writing ability include

Elliot (as cited in Attali & Burstein, 2006) and studies by Peterson and by Landauer, Laham,

Rehder, and Schreiner (both cited in Powers, Burstein, Chodorow, Fowles, & Kukich, 2000). A

model for the current study was Powers et al. (2000). This study looked at correlations between e-

rater and human scores on two essay tasks from the GRE

General Test with several other

indicators of writing ability: (a) two samples of writing prepared as course assignments, (b) self-

evaluations of writing, (c) self-reported grades in writing-intensive courses, (d) self-reported

documentation of accomplishments in writing, and (e) success with various kinds of writing. The

researchers found modest but significant correlations between e-rater scores and most of the

indicators, with the highest correlations being with evaluators’ grades on course assignments.

E-rater did not fare as well as human raters in these correlations, one possible explanation being

that the version of e-rater used in the study did not focus to the same degree as human raters on

aspects of writing reflected in the nontest indicators and that e-rater tended not to assign extreme

scores. This study suggests that the validity-related inference of generalizability across raters may

not be fully supported for e-rater, at least in earlier versions.

Automated Scoring and Nonnative Writing

In addition to the issues raised above, there is another set of validity-related issues

surrounding the use of automated scoring for nonnative writers. E-rater, like other automated

scoring systems, was designed initially with a population of native speakers in mind. For it to be

accepted as a valid method of scoring nonnative speakers (NNS) of English, particularly the

population of TOEFL examinees, a number of considerations will need to be dealt with. One issue

is computer familiarity of examinees—since automated scoring depends on digital rather than

paper-and-pencil tests, evidence must be presented that the lack of keyboarding skills does not

lead to construct-irrelevant variance. The issue of computer familiarity is of particular importance

to the TOEFL because of variable access to computer technology in the different countries that

comprise the population of TOEFL examinees. The question of computer familiarity as it relates

to the TOEFL was first discussed in Kirsch, Jamieson, Taylor, and Eignor (1998), who found a

relationship between level of computer familiarity and TOEFL scores.

Wolfe and Manalo (2004) found an interaction between language proficiency and chosen

medium (handwriting or word processing), with lower proficiency students performing better if

they handwrote their essays and higher proficiency students performing better if they input their

essays on the computer. Wolfe and Manalo expressed concerns that groups traditionally

associated with low computer familiarity or higher computer-related anxiety (e.g., females,

examinees from developing countries, and older examinees) tend to choose handwriting over

word processing. If these examinees are required to use computers in writing assessments they

may have to perform a “double translation,” which increases the cognitive demands of the task—

Not only do they have to translate from their native language into English, but they also have to

translate from English into unfamiliar keystrokes. This additional cognitive load is a potential

source of construct-irrelevant variance, and more research is needed to explore this issue.

Another set of concerns related to the assessment of NNS writing is the question of

whether the features used to score essays, particularly in the areas of grammar, usage, and

vocabulary, are in fact the features of language that are problematic for NNS. Since the Criterion

analysis tools used to detect errors in grammar and usage are intended to focus on the kinds of

errors typically made by native speakers rather than those found in NNS texts (Burstein et al.,

2003), the errors extracted by Criterion and thus used in e-rater are not necessarily those that

appear in NNS writing. However, it should be noted that work is being done to improve

identification of typical NNS errors such as prepositions and articles (Chodorow, Gamon, &

Tetreault, 2010).

Another issue to be taken into consideration is the fact that the TOEFL differs from other

writing tests used for screening and university admission in that it is a test of language proficiency

rather than an aptitude test or a test of analytical thinking. Indeed, research on second-language

writing (e.g., Cumming, 1989; Sasaki & Hirose, 1996) suggests that language proficiency and

writing ability are separate, although related, constructs. While predictive validity studies of tests

such as the SAT, GMAT, and GRE Tests presume that the ability measured by the test is more or

less stable, this is not the case for the TOEFL. As Simner (1999) noted:

The major purpose of using the TOEFL as an admissions screening device is not to

determine how well a student performs in English at the time the TOEFL is taken, but

instead to determine how well the student is likely to perform in the future, which

typically means some 8-10 months later after the student has arrived on campus and is

immersed in an English speaking environment. Hence, the evidence needed to support the

TOEFL as a screening device is evidence in favor of predictive validity. (p. 287)

Studies of the predictive validity of the TOEFL have had mixed results. A few studies

have looked at the relationship between TOEFL scores and indicators of success such as

graduation rate, GPA, or GPA after the first 9 credit hours. For example, Ayres and Peters (1977)

found that TOEFL scores were predictive of graduate grade point average (GGPA) among Asian

students in science and engineering, and that a combination of TOEFL and the verbal section of

the GRE General Test predicted success in program completion. On the other hand, Neal (1998)

found no relationship between TOEFL scores and GGPA. Studies by Light, Xu, and Mossop

(1987), Xu (1991), and Yule and Hoffman (1990) also found little evidence of a relationship

between TOEFL scores and academic success. It should be noted that these studies were based on

the total TOEFL score, not the writing score in particular; little attention has been paid to the

predictive validity of the TOEFL writing test specifically.

One reason that the TOEFL in general does not consistently demonstrate predictive

validity is that language proficiency in itself is only one of many factors that influence success in

university studies. Another reason is that requirements for language skills and proficiency may

vary by college and major, so that students with lower TOEFL scores may be successful in some

areas and not in others. A third reason is that different levels of support for international students

with limited proficiency are offered at different institutions. For these reasons it is not likely that

TOEFL scores by themselves will ever be strongly predictive of academic success, beyond

providing a threshold (floor) below which students have a strong probability of not being

successful because of limitations in their language proficiency.

To summarize, using automated scoring systems for the TOEFL, which is intended for

nonnative writers, brings up certain validity questions beyond those that may apply to tests of

writing for native speakers. The research described here does not attempt to answer all of these

questions; however, these questions should be kept in mind when interpreting research results and

planning additional research in this area.

Task Variability in Writing Assessment

The advantages of a direct test of writing, as opposed to a more indirect test such as a

multiple-choice test, come with the serious disadvantage of a limited ability to sample the domain

adequately, so that writing tests are often limited to a single 30-minute prompt. It is therefore

critical to ensure that differences across prompts are minimized so that examinees have an equal

chance of performing successfully on all potential tasks. Task variability can affect performance

in a number of ways (see Weigle, 2002, chap. 4, for an overview); in the words of Purves (1992):

“different tasks present different problems, which are treated differently by students and judged

differently by raters” (p. 112).

Because each TOEFL examinee writes on only a single independent topic, there has been

little opportunity to investigate the reliability of scoring (human or automated) across different

topics using data from the same people. Most studies of writing prompts from the TOEFL, or its

predecessor, the Test of Written English™ (TWE

), rely on other means of analyzing prompt-

related differences. For example, an early study done of essays written for the TWE found small

but significant differences across eight different prompts, studying the operational administration

of these prompts worldwide (Golub-Smith, Reese, & Steinhaus, 1993). In more recent studies

applying e-rater to TOEFL essays, neither Burstein and Chodorow (1999) nor Chodorow and

Burstein (2004) used essays written by the same people in their studies of applying e-rater to

nonnative speakers of English. Attali (2007, 2008) is a notable exception, in that he investigated

the reliability of human and e-rater scores of essays for repeat test takers; however, Attali’s study

did not look specifically at task-related differences.

Despite not using essays written by the same candidates, Chodorow and Burstein (2004)

found that scores of human raters were more variable across prompts than were automated scores,

and also found a significant main effect of prompt on essay scores, significant main effects of

rater (human vs. two versions of e-rater) and native language, and interactions between prompt

and rater, rater and language, and language and prompt. It appears, therefore, that investigating

effects of differences among TOEFL prompts is still an area where more research is needed.

The standardized writing features included in e-rater offer an opportunity to investigate

differences in the textual structure of essays written to different prompts by the same candidates.

Attali and Burstein (2006) used essays from the Criterion database written by students from 6

through 12

grades to investigate reliability across essay prompts, and found that e-rater and

human scores were very highly correlated. Furthermore, they found that certain features used by

e-rater had moderate test-retest reliabilities, most of which were in the mid .40s. No study to date

has looked at differences in e-rater feature scores across prompts of the TOEFL.

To summarize the literature review, I will return to the TOEFL interpretive argument

articulated by Chapelle et al. (2008). In terms of generalizability, the literature suggests that

improvements in e-rater have reduced the gap between automated scores and human scores

considerably, though some questions remain about this equivalence for the TOEFL in particular.

Furthermore, there is little research comparing performance by the same students on different

TOEFL writing tasks both on overall scores (human and e-rater) and e-rater features. In terms of

extrapolation, there is a dearth of research addressing the relationship between the construct of

writing assessed by the TOEFL (and embodied in the scoring rubric used by human raters and the

algorithms used by e-rater) and the actual writing performance of students outside the testing

construct. The study reported on here attempts to address these issues.

Method

Research Questions

This study addresses the following research questions:

1. What are the relationships between overall e-rater and human scores on TOEFL iBT

independent writing tasks and other indicators of writing ability (self-assessment of

writing ability, instructor assessment of writing ability, and independent rater

assessment on discipline-specific writing tasks)?

2. What are the relationships among specific features analyzed by e-rater and these

indicators of writing ability?

3. How consistent are the scores generated by e-rater (both the total scores and scores for

individual features) and human raters across two different writing tasks?

Participants

Data were gathered from 386 nonnative English-speaking students at eight different

institutions in the US over a 15-month period, from October 2006 through December 2007 (see

Table 1 for participant characteristics). Participants were recruited from the international student

populations at the following institutions: Iowa State University, Georgia State University,

Michigan State University, Pace University, the University of California at Los Angeles, Purdue

University, Portland State University, and the University of Minnesota. The original intention was

to test matriculated students only, but at one institution 26 students enrolled in that university's

English Language Institute were included in the participant pool. Participants were each paid $50

for their participation, in the form of a gift card for their university bookstore.

Table 1

Participant Characteristics

Characteristic N

Total 386

Age Mean (years): 24.86

Range: 18–47

Gender Female

Male

Unknown

222

163

Native

language

Chinese

Korean

Japanese

Spanish

Vietnamese

Russian

French

Turkish

Other

158

Status Graduate

Undergraduate

Other (ELI

/not specified)

199

159

Field of study Business

Social Sciences

Engineering

Humanities

Natural Sciences

Computer Science

Education

Applied Sciences

Health Sciences

Mathematics

Missing/Other

English language institute.

Materials

The following data were collected:

Essays responding to TOEFL iBT tasks. Two independent writing tasks, provided by

ETS for this study, were administered to participants. One prompt (hereafter referred to as Topic

1) asked students to discuss whether too much emphasis is spent on personal appearance and

fashion, and the other (hereafter referred to as Topic 2) dealt with the importance of planning for

the future. The order of prompts was counterbalanced so that half of the participants received one

prompt first and half received the other prompt first.

Self-assessment of writing ability. A web-based survey adapted from Allwright and

Banerjee (1997) was created using SurveyMonkey, an online survey development tool (see

Appendix A for the survey). The student survey had four sections. First, students were asked to

rate their ability to write, read, speak, and understand English and also to compare their ability to

use English for coursework with their ability to use English outside of school. Next, students were

given a list of nine problems that students sometimes have with writing and were asked to

indicate how often they experienced these problems. In the third and fourth sections, respectively,

students were asked to indicate how often they experienced specific problems related to other

aspects of English (e.g., speaking, reading, and participating in class discussions) and to

nonlanguage related problems (e.g., time management and understanding the subject matter). In

each section students could provide open-ended comments as well.

To validate the student survey, a factor analysis of the survey data (excluding the overall

self-evaluation variables) was conducted using principal components analysis with varimax

rotation (see Appendix B). The factor analysis revealed three main factors similar to the intended

factors, with the exception of three writing items that loaded on the third factor instead of the

writing factor. Accordingly, the following three scales were constructed: (a) Writing problems (6

items, α = .82), (b) Other language problems (5 items, α = .81), and (c) Other problems (7 items,

α = .80).

Instructor assessment of writing ability. Participants were asked to provide names and

contact information for two instructors familiar with their written work. These instructors were

contacted by e-mail and asked to complete an online survey (see Appendix C for the survey). The

instructor survey was similar in structure to the student survey, with sections asking instructors to

rate the student's overall performance in the course, the student's writing ability, oral ability, and

overall level of English, and their perceptions of the impact of linguistic and nonlinguistic factors

on the student's performance. Instructors also were invited to make open-ended comments in each

section of the survey.

As with the student survey, a factor analysis was conducted to explore the structure of the

survey (see Appendix D). Two scales were constructed, one for language-related problems (9

items, α = .96) and one for nonlanguage related problems (5 items, α = .91). Unlike the student

survey, which had very few missing responses, many instructors chose the option "no opportunity

to judge" on several items, which was recorded as a missing response. Therefore each scale score

was calculated as the average of nonmissing scores rather as the total of the nonmissing scores.

Nontest writing samples. Participants were asked to provide two samples of writing for

courses in which they had been enrolled within the past 6 months. Participants were encouraged

to provide writing samples from their major courses, but many only had writing samples from

writing classes (i.e., English composition or English as a Second Language [ESL] courses).

Participants were asked to provide, if possible, one sample that represented their typical writing

and one that was not as good as their typical writing. The rationale for this request was based on

Powers et al.'s (2000) observation that students tend to submit their best samples, rather than

typical samples; thus an attempt was made to obtain writing that was more representative of

typical course-related writing. Approximately half the collected samples were from major courses

and half were from English composition or ESL courses. Samples of student writing are found in

Appendix E.

Participant information sheet. The participant information sheet (see Appendix F)

served two functions. First, it provided an opportunity to collect basic demographic information

from students. Second, it served as the vehicle for collecting contact information for students’

instructors and information about the two writing samples students were asked to provide. This

information included the name of the course for which the paper was written, their estimation of

the strength of the writing, and the types of assistance, if any, students had received on the paper.

Procedures

When participants signed up for the study they were given the information sheet and asked

to bring it back completed on the test date, along with their two writing samples. When they

arrived at the testing site, they logged on to a secure website, where they took the student survey

and then the writing test. The two writing topics were presented in random order. When the

students had completed all study requirements, including supplying contact information for their

instructors and submitting their writing samples, they were compensated and then dismissed.

Following student data collection, instructors were contacted by email with a request to

complete the instructor survey. Reminders were sent to instructors after 2 weeks; in some cases a

second reminder email was sent to instructors who had not yet completed the survey. A total of

410 instructors completed the survey; of the 386 student participants, 186 (48%) had one

instructor response, 112 (29%) had two, and 88 (23%) had no instructor information.

Scoring of iBT essays. All TOEFL iBT essays were sent to ETS for scoring with the

current version of e-rater. The generic or "program specific" e-rater model uses eight features and

was built on the responses of tens of thousands of examinees to more than 25 TOEFL prompts,

including the two prompts used in this study (Attali, 2007). The only prompt-specific

customization of the model was that the machine scores were scaled to have the same mean and

standard deviation as human ratings for the specific prompt. The e-rater features used in the study

were the features described above, except that the two prompt-specific vocabulary scores and

essay length were not included.

Each TOEFL iBT essay was also scored by two trained raters using the TOEFL scoring

rubric (see Appendix G). The TOEFL iBT essays were also scored by trained raters.

Approximately half of the scripts were scored by experienced raters certified by ETS; a total of

four raters participated in the first round of rating. The second half of the scripts were rated by

raters hired by the author; they were experienced ESL teachers who had rated other writing

assessments but not TOEFL essays. These raters completed the ETS online training before rating

the scripts but were not certified by ETS. The author also rated any essays that received scores

from the two human raters that were more than one point apart; however, all analyses presented in

this report are based on the scores of the original two raters. For all analyses involving individual

raters, ratings have been randomly assigned to Rater 1 or Rater 2. Table 2 shows interrater

reliability statistics for these ratings; overall, they are comparable to statistics found in similar

studies (e.g., Attali, 2007; Attali & Burstein, 2006). For example, Attali and Burstein (2006)

reported exact agreement rates of two human raters of .59 and one human rater with e-rater of .58.

Pearson correlations between individual raters (i.e., not ratings) ranged from .54 to .83;

correlations of individual raters with e-rater scores ranged from .66 to .75. Across the two topics,

correlations were as follows: Rater 1, r = .62; Rater 2, r = .58; Average rating, r = .71; e-rater, r =

.79. This suggests that e-rater is somewhat more reliable than human ratings in terms of alternate-

forms reliability.

Table 2

Interrater Reliability of e-rater and Human Rater Scores

Topic 1

Topic 2

Overall

Rater 1/Rater 2

Pearson correlation

.67

.64

.65

Exact agreement/exact + adj. agreement

.57/.97

.47/.94

.52/.96

Rater 1/e-rater

Pearson correlation

.67

.75

.71

Exact agreement/exact + adj. agreement

.52/.97

.51/.98

.52/.98

Rater 2/e-rater

Pearson correlation

.71

.72

Exact agreement

/exact + adj. agreement

.56/.96

.49/.97

.53/.97

Average of 2 HR/ e-rater

Pearson correlation

.76

.81

.79

Exact agreement

/exact + adj. agreement

.73/.95

.76/.97

.74/.96

Note. Exact agreement means that the two raters gave exactly the same score; adjacent agreement

means that the two scores differed by one point or less. For analyses involving e-rater, scores

were rounded off to the nearest whole number. Since the average of two human rater scores was

not always a whole number, agreement was counted as exact if the rounded e-rater score was

within ½ point of the average of two raters.

Scoring of submitted writing samples. The course writing samples provided by students

were scored by a pool of trained raters on a scale designed for the study consisting of two

subscales: content and language (see Table 3). Each sample was rated by two raters, with a third

rater adjudicating if the two raters differed by more than a point on either scale. The reported

score is the average between the two raters. Pearson correlations between the two (averaged)

ratings on each scale across samples were .51 for content and .58 for language; within-samples

correlations between content and language were .78 for Sample 1 and .79 for Sample 2. The

original scale included two score points below Fair but as no submitted samples were judged to

be below Fair these two points were excluded from the final rating scale.

Table 3

Scoring Rubric for Submitted Writing Samples

Score

Content

Language

6 – Excellent

Issues dealt with fully, clear

position, substantive arguments,

balanced ideas with full support and

logical connection, strong control of

organization

Excellent control of language with

effective choice of words, sophisticated

range of grammatical structures and

vocabulary, few or no errors

5 – Very good

Issues dealt with well, clear

position, substantive arguments,

generally balanced ideas with

support and logical connection,

good control of organization,

occasional repetition, redundancy,

or a missing transition

Strong control of language, read

smoothly, sufficient range of

grammatical structures and vocabulary

with occasional minor errors

4 – Good

Issues discussed but could be better

developed, positions could be

clearer and supported with more

substantive arguments, appropriate

organization, with instances of

redundancy, repetition, and

inconsistency

Good control of language with adequate

range of grammatical structures and

vocabulary, may lack fluidity, some

grammatical errors

3 – Fair

Issues discussed, but without

substantive evidence, positions

could be clearer and arguments

could be more convincing, adequate

organization, ideas are not always

balanced

Fair control of language with major

errors and limited choice of structures &

vocabulary, errors may interfere with

comprehension

Data analysis. For Research Question 1 the relationship between essay scores and

criterion variables was investigated primarily through correlations. Pearson correlations between

criterion variables (student survey variables, instructor survey variables, and ratings on nontest

writing samples) and ratings on TOEFL iBT essays were computed separately for each prompt as

follows:

• E-rater (ER); 1 rating per prompt (2 total)

• Each human rating (1 HR); 2 ratings per prompt (4 total)

• The average between the two raters (2 HR); 1 averaged rating per prompt (2 total)

• The average of each human rating and e-rater (1 HR/ER); 2 averaged ratings per

prompt (4 total)

The average of the correlations in each category across rater combinations and prompts

(single human rater, average of two human raters, e-rater, and average of one human rating and e-

rater) is reported in the results.

Where appropriate, differences in the magnitude of correlations between e-rater scores

and the average of two human rater scores, respectively, with criterion variables were calculated

using procedures outlined in Cohen and Cohen (1983, p. 57; see Urry, 2003, for the SPSS

syntax).

Operationally, e-rater is used as one of the two raters for the TOEFL; however, it was

designed to emulate the average of two raters’ scores. For this reason the average between the

two raters was felt to be the most appropriate human rating to compare with e-rater for this

analysis.

For Research Question 2 the e-rater feature scores were averaged across the two writing

prompts, and Pearson correlations were calculated among the averaged features scores and

criterion variables (global self and instructor ratings of language ability and scores on nontest

writing samples). Finally, for Research Question 3 paired t-tests were conducted to compare

scores on the two prompts in terms of individual ratings, the average human rater scores, e-rater

total scores, and feature scores by prompt. In addition, a repeated-measures ANOVA was

conducted with rater and prompt as independent variables and score as the dependent variable. All

statistical analyses were carried out using SPSS Versions 15 and 16.

Research Question 1: Results and Discussion

Research Question 1 (regarding the relationship between human and e-rater scores and

other indicators of writing ability) was addressed through correlations between scores on iBT

essays (human and e-rater) and a variety of criterion variables. As noted above there were three

main data sources apart from the TOEFL essays: student surveys, instructor surveys, and ratings

on other writing samples. For the student and instructor surveys, correlations were calculated

between TOEFL essay scores and both the global evaluation items and the survey scales as

described above. For the additional writing samples, correlations were calculated between TOEFL

essay scores and scores on content and language.

Results

Relationships between essay scores and student self-assessment. The relationships

between scores on TOEFL iBT essays and student survey variables are presented in two sections:

First student overall self-evaluations of language ability are discussed, and then specific problems

that are related to language as well as those that are not. In the survey, students were asked to rate

their ability in the skills of writing, speaking, listening, and reading on a scale of 1 to 4.

Descriptive statistics for these variables are found in Table 4, and correlations among these

variables and the problem scales from the survey are found in Table 5. As Table 4 shows, students

rated themselves the highest in receptive skills (reading and listening) and lowest in productive

skills (writing and speaking). Table 5 shows that the global self-evaluation variables have

moderately strong correlations with each other (.59 to .69) but are less strongly related to the three

problem scales (.33 to .49); the correlations among the scale variables themselves range from .52

to .60.

Average correlations between combinations of e-rater and human scores on the TOEFL

iBT essays and self-evaluation variables are found in Table 6. As noted above, these correlations

are averaged across the two prompts for e-rater and the average human rater score and across both

raters and prompts for single human rater scores. The correlations are moderate, with higher

correlations for reading and writing than for listening and speaking.

Table 4

Descriptive Statistics of Global Self-Evaluation Variables

Mean

Self-Evaluation Writing

382

2.63

0.83

Self-Evaluation Reading

382

2.96

0.84

Self-Evaluation Listening

378

3.05

0.84

Self-Evaluation Speaking

381

2.66

0.85

Table 5

Correlations Among Global Self-Evaluation Variables and Student Survey Scales

Global Self-Evaluation variable

Self-Evaluation Writing

—

.68

.59

.68

.48

.41

.40

Self-Evaluation Reading

—

.69

.60

.43

.49

.42

Self-Evaluation Listening

—

.65

.37

.49

.33

Self-Evaluation Speaking

—

.41

.49

.33

Writing Problems Scale

—

.52

.57

Language Problems Scale

—

.60

Other problems

344

9–28

23.67

3.36

.80

.25

.23

.26

Note. All individual correlations significant at p < .01 except between e-rater and other language

problems, which was significant at p < .05 on Topic 1 and not significant on Topic 2. 1 HR =

individual human rating; 2 HR = average between the two raters; 1 HR/ER = average of each

human rating and e-rater.

Correlations of e-rater and average human rater scores with criterion variables were not

significantly different.

Cronbach's alpha.

instructors were English/ESL teachers, .12 (ns) when neither instructor was an English/ESL

teacher, and -.23 (ns) when the two were an English/ESL instructor and a content instructor

This suggests that the language demands of different English/ESL courses may be more similar

to each other than they are to those of content area courses or than those of content courses are

to each other. It also explains the near-zero correlations when instructors are not grouped in this

way.

Of the 296 instructors who were the first respondents to the survey, slightly more than

50% (153) were English, writing, or ESL instructors, and the rest (143) were subject instructors.

The responses for these two groups were analyzed separately for a number of reasons. As noted

above, perhaps these two groups appeared to respond differently to the survey items because

writing courses in general, and ESL writing courses in particular, focus on the mastery of

language-related skills rather than specific knowledge about an academic discipline. In the

courses, assignments are adjusted with respect to the presumed writing and/or language ability of

students in the class. In a lower-level ESL course, for example, the readings and writing

assignments may be shorter and simpler than for a higher level writing course or a course in an

academic discipline such as philosophy or business. Thus the responses to an item that asks

instructors to judge, for example, whether a student has problems understanding course

assignments will likely be different between these two groups of instructors.

For the purposes of this study, perhaps the most important reason to distinguish between

these two instructor groups is that the readings, assignments, and other demands of content course

areas represent, in fact, the target language use situation (Bachman & Palmer, 1996) of the

TOEFL. That is, test users (e.g., admissions officers) are interested in knowing how well

prospective students will be able to use English in academic disciplines such as biology,

economics, or psychology. Thus in the investigation of the predictive validity of the TOEFL it is

particularly important to distinguish between instructors of content courses and English or ESL

instructors when examining instructor perceptions of NNS performance in their courses.

Like the student survey, the instructor survey included both global assessments of language

proficiency and items asking about specific problems that students may face. Descriptive statistics for

the proficiency variables are found in Table 9, and intercorrelations between these variables and the

instructor survey scale variables are found in Table 10.

Table 9

Descriptive Statistics for Instructor Ratings of Overall Performance and Proficiency

Subject

English

Overall

Mean

Overall academic performance

138

3.23

0.63

153

2.99

0.79

291

3.11

0.73

Writing ability evaluation

142

3.06

0.76

146

2.82

0.87

288

2.94

0.82

Oral proficiency evaluation

143

3.16

0.80

153

2.92

0.86

296

3.04

0.84

General evaluation of English ability

143

3.14

0.70

151

2.89

0.80

294

3.01

0.76

Table 10

Correlations Among Instructor Survey Variables

1. Writing ability

—

.61

.83

.44

.40

2. Oral proficiency

—

.79

.40

.46

3. Overall ability

—

.42

.48

4. Language problems

—

.43

5. Other problems

—

A few observations can be made from these tables. First, Table 9 shows that ratings by

subject area instructors were higher than ratings by English instructors; this is not surprising, since

many students in the study were specifically placed into English/ESL classes because of a perceived

need to improve their English. Table 10 shows that the instructor evaluations of different aspects of

English proficiency were more highly correlated than the similar self-evaluation variables discussed

earlier; however, the relationship between the overall evaluations of proficiency and the scale

variables were approximately the same for both the student and instructor surveys.

Correlations between the overall instructor evaluation variables and the TOEFL iBT ratings

are found in Table 11. Interestingly, scores on TOEFL iBT essays correlated more strongly with

subject area instructor ratings of student proficiency than with those of English instructors; this

result may be due to the differences in comparison groups noted above. E-rater correlations were

slightly lower than human rater correlations, but these differences were significant only for Topic 1

for the overall evaluation (t = -2.91, df = 285, p < .01, effect size = .08).

Table 11

Average of Correlations Between Scores on TOEFL iBT Tasks and Instructor Ratings of

Overall Performance and Proficiency

e-rater

1 HR

2 HR

1 HR/ER

Subject (n = 138–143)

Overall academic performance

.23

.24

.25

.26

Writing ability

.30

.34

.37

.35

Oral proficiency

.36

.39

.41

Overall English ability

.38

.42

.46

.44

English (n = 146–153)

Overall academic performance

.13

.14

Writing ability

.22

.20

.21

.22

Oral proficiency

.16

.19

.20

.18

Overall English ability

.27

.29

Total (n = 288–296)

Overall academic performance

.21

.22

.23

Writing ability

.28

.29

.32

.31

Oral proficiency

.27

.31

.33

.31

Overall English ability

.34

.36

.39

.38

Note. n refers to the sample sizes for individual correlations, which vary within each category

because of missing data. For sample sizes 138–153, individual correlations below approximately

.16 are not significant, between .17 and .21 significant at p < .05, and above .21 at p < .01. For

samples sizes 288

–296, individual correlations above .16 are all significant at p < .05. 1 HR =

individual human rating; 2 HR = average between the two raters; 1 HR/ER = average of each

human rating and e-rater.

As noted earlier, the instructor survey consisted of nine language-related questions and five

nonlanguage-related questions. Descriptive statistics for these scales are found in Table 12, and

correlations with TOEFL iBT essay ratings are presented in Table 13.

As was the case with the overall proficiency and performance variables, the scale scores

from subject area instructors were generally higher than those from English instructors. In

addition the correlation between the language impact scale and TOEFL essay scores was

significantly higher for the average of two human raters than for e-rater for the subject area

Table 12

Descriptive Statistics for Instructor Survey Scale Variables

Range

Mean

Total

Language impact

296

–

3.56

0.98

Impact of other factors

279

–

4.11

0.76

Subject

Language impact

143

–

3.72

0.95

Impact of other factors

134

–

4.18

0.73

English

Language impact

153

1–5

3.41

0.98

Impact of other factors

145

–

4.05

0.80

Table 13

Average Correlations of Instructor Survey Scale Variables With e-rater and

Human Ratings of TOEFL iBT Essays

e-rater

1 HR

2 HR

1 HR/ER

Subject (n = 133–143)

Language impact

.15

.31

.33

.26

Impact of other factors

.16

.21

.23

.20

English (n = 145–153)

Language impact

.18

.15

.17

.18

Impact of other factors

.00

.01

.00

Total (n = 279–296)

Language impact

.21

.26

.28

.25

Impact of other factors

.09

.12

.13

.11

Note. n refers to the sample sizes for individual correlations, which vary within each category

because of missing data. For sample sizes 133–153, individual correlations below approximately

.16 are not significant, between .17 and .21 significant at p < .05, and above .21 at p < .01. For

samples sizes 279–296, individual correlations above .16 are all significant at p < .05. 1 HR =

individual human rating; 2 HR = average between the two raters; 1 HR/ER = average of each

human rating and e-rater.

Correlations of e-rater and average human rater scores with this variable were significantly

different for both topics; no other correlations were significantly different.

instructors (Topic 1: t = -2.32, df = 138, p < .05, effect size = .09; Topic 2: t = -3.15, df = 138, p <

.01, effect size = .07) but not for the English instructors.

Relationship between essay scores and scores on student-supplied writing samples.

The third main indicator of writing ability examined in this study were the ratings on content and

language for course-related writing samples provided by student participants. Descriptive

statistics and interrater agreement statistics between the first two (unadjudicated) scores for these

variables are found in Table 14. Recall that a third rater was used in those cases where the scores

diverged by more than a point; the data in Table 14 do not include any of these third ratings. The

reported scores in the table are the average scores between two raters.

As noted above, approximately half of the writing samples were from English or writing

courses (e.g., ESL writing courses, technical writing) and half were from subject-area courses (e.g.,

chemistry, anthropology, applied linguistics). Recall that subject-area writing samples tended to be

higher in both register and cognitive demands than English writing samples. Scores on subject-area

papers were slightly higher than those on English/writing papers for both content (t = -8.61, df =

367, p = .000) and language (t = -8.58, df = 367, p = .000).

Correlations between scores on writing

samples with e-rater, single human rater scores, and two human rater scores are found in Table 15.

The table presents correlations for all samples combined as well as for samples divided into subject

area versus English. There were no significant differences in the magnitude of correlations between

the criterion variables and e-rater versus the average human score.

Note that the correlations in Table 15 are averaged across writing samples (Sample 1 and

Sample 2) and prompts (Topic 1 and Topic 2). As the table shows, both e-rater and human rater

scores were more highly correlated with scores on English papers than on subject area papers, and

were more highly correlated with the language scores than the content scores. Overall the

correlations tended to be higher between scores on nontest writing samples and TOEFL

independent writing tasks than for other indicators of writing ability; furthermore, the correlations

with e-rater scores appear to be more similar to those with human scores on this measure than

with the other indicators.

Summary of results for Research Question 1. As a summary of the highest correlations

between e-rater and essay scores and criterion variables, Table 16 displays the average

correlations between e-rater, the average human rater score, and all variables where at least one

correlation is greater than or equal to .3, sorted in descending order of the average human rater

score within each data source. Across most variables in the three data sources, the average

correlations for e-rater and individual human raters were very similar, although the differences

between e-rater and the average of two human rater scores were significant on the self-evaluation

variables and some of the instructor survey variables. The most robust difference between e-rater

and human ratings was in the language impact scale for subject area instructors.

Table 14

Descriptive Statistics and Interrater Agreement Statistics for Submitted Writing Sample Scores

Descriptive statistics

Interrater agreement indices

Range

Mean

Exact

Exact + adj.

Kappa

Content

748

3–6

5.13

0.70

.70

69.6

99.1

.51

Language

748

3–6

4.89

0.72

.71

64.7

99.9

.46

Note. Exact agreement means that the two raters gave exactly the same score; adjacent agreement

means that the two scores differed by one point.

Table 15

Average Correlations of Submitted Writing Sample Scores With TOEFL iBT Task Scores by

Content Area of Writing Samples

e-rater

1 HR

2 HR

1 HR/ER

Content

English

.39

.34

.37

.39

Subject

.23

.21

.24

.23

Total

.38

.36

.40

Language

English

.41

.38

.42

.43

Subject

.29

.30

.33

.32

Total

.42

.46

.45

Note. All individual correlations significant at p < .01. 1 HR = individual human rating;

2 HR = average between the two raters; 1 HR/ER = average of each human rating and e-rater.

Table 16

Summary of Highest Average Correlations From Different Data Sources

Source

Variable

1 HR

2 HR

1 HR/ER

Self-Evaluation Writing

.36

.39

.43

.41

Self-Evaluation Reading

.36

.38

.42

.40

Self-Evaluation Speaking

.26

.32

.35

.31

Self-Evaluation Listening

.23

.31

.33

.29

Writing Problems Scale

.30

.27

.29

.31

General Evaluation of English

Ability (subject)

.38

.42

.46

.44

Oral Proficiency Evaluation (subject)

.36

.39

.41

General Evaluation of English Ability

(all)

.34

.36

.39

.38

Writing Ability Evaluation (subject)

.30

.34

.37

.35

Language Impact Scale (subject )

.15

.31

.33

.26

Oral Proficiency Evaluation (all)

.27

.31

.33

.31

Content (all essays)

.38

.36

.40

Content (English essays only)

.39

.34

.37

.39

Language (subject essays only)

.29

.30

.33

.32

Note. Correlations above .30 are in boldface. SS = student survey; IS = instructor survey; WS =

writing samples; 1 HR = individual human rating; 2 HR = average between the two raters; 1

HR/ER = average of each human rating and e-rater.

Correlation between criterion variable and 2 HR average was significantly higher than

corresponding correlation with e-rater on one topic.

Correlations between criterion variable and

2 HR average were significantly higher than corresponding correlations with e-rater on both

topics.

Discussion

Relationship between e-rater and human scores on TOELF iBT essays. From the

results of the study it is clear that e-rater scores and human scores are highly correlated and thus

can be said to be measuring highly similar constructs. From a practical perspective there seems to

be little or no difference in scores between human raters and e-rater, and in fact the alternate-

forms reliability of e-rater is somewhat superior to that of human raters in this study. However,

there were some differences between e-rater and human scores on a few of the variables. These

variables tended to be related to overall language proficiency rather than writing per se. For

example, although the correlations between essay scores and self-evaluations of reading and

writing were higher than those between essay scores and self-evaluations of listening and

speaking, the correlations between human ratings and the latter self-evaluation scores were

significantly higher than the corresponding correlations with e-rater scores. This finding suggests

the possibility that the e-rater algorithm may not be as sensitive as human raters to certain

markers of language proficiency.

The most striking difference between e-rater scores and the corresponding human scores is

found in the relationship with subject instructors’ ratings of the problems that their NNS students

have that are related to language proficiency. One possible explanation for this result may be

found in the research finding that essay raters do not base their scores strictly on the wording of a

specific scale (see Eckes, 2008, for a recent review of the literature on rater behavior). For

example, Lumley (2002) noted that raters’ judgments seem to be based on “some complex and

indefinable feeling about the text, rather than the scale content” and that raters form “a uniquely

complex impression independently of the scale wordings.” Part of this complex impression is

related to raters’ expectations of writers, often based on their own teaching and previous rating

experience (see Weigle, 2002, for a discussion of this issue). Thus, raters may be influenced by

their notions of the situations in which students would find themselves and may base their ratings

in part on their intuitions about language issues that are problematic in content courses. This in

turn may have aligned their scores more closely with instructor ratings.

Relationships between scores on TOEFL iBT essays and criterion variables. As for

considerations of criterion-related validity, correlations between essay scores and other indicators

of writing ability were generally moderate, whether they were scored by human raters or e-rater.

These moderate correlations are not unlike those found in other criterion-related validity studies

(see, for example Kuncel, Hezlett, & Ones, 2001, for a meta-analysis of such studies of GRE

Tests). They are also similar to or higher than those presented in Powers et al. (2000), which

compared e-rater scores of GRE essays with a variety of other indicators. The correlations in that

study ranged from .08 to .30 for a single human rater, .07 to .31 for two human raters, and .09 to

.24 for e-rater. Possible explanations for the difference in magnitude of these correlations include

improvements in e-rater since the Powers et al. study was written and difference in the writing

constructs measured by the GRE and the TOEFL (Lee, 2006).

The highest correlations tended to be for global measures of global language proficiency

rather than specific aspects of writing ability, suggesting that the TOEFL iBT independent task may

be more useful as a measure of general language proficiency than of academic writing ability.

Research Question 2: Results and Discussion

Results

To answer Research Question 2 (regarding relationships among specific features analyzed

by e-rater and indicators of writing ability), the eight e-rater feature scores were averaged across

the two topics. Correlations were calculated between the averaged e-rater feature scores, the

overall self-evaluation variables from the student survey, the overall evaluation of language

proficiency from the instructor survey, and the writing sample scores. Results of these analyses

are presented in Tables 17 to 19.

Table 17

Correlations of Averaged e-rater Feature Scores With Self-Evaluation of Language Skills

e-rater feature

Writing

Reading

Listening

Speaking

Vocabulary

.33**

.24**

.19**

.22**

Style

.27**

.29**

.19**

.22**

Usage

.27**

.25**

.19**

Grammar

.22**

.23**

.21**

.15**

Mechanics

.22**

.15**

.08

Word Length

.19**

.15**

.08

.11*

Organization

.14**

.19**

.04

.07

Development

.13*

.14**

*p <. 05. **p < .01.

Table 18

Correlations of Averaged e-rater Feature Scores With Composite Instructor Evaluation of

English Ability

e-rater feature

Subject instructor

evaluation (n = 140)

English instructor

evaluation (n = 144)

Usage

.37**

.25**

Grammar

.27**

.14

Style

.25**

.19*

Organization

.19*

–.02

Mechanics

.15

.17*

Development

.13

.20*

Vocabulary

.12

.19*

Word Length

.06

.21*

*p < .05. **p < .01.

Table 19

Correlations of Averaged e-rater Feature Scores With Averaged Content and Language Scores

on Submitted Writing Samples

Content average

(two writing samples)

Language average

(two writing samples)

Vocabulary

.37**

.44**

Grammar

.33**

.41**

Style

.31**

.35**

Mechanics

.26**

.31**

Word length

.25**

.29**

Usage

.24**

.29**

Development

.21**

.16**

Organization

.12*

.15**

*p < .05. **p < .01.

The variables that were most closely related to student self-perception of all four language

skills were vocabulary, style, usage, and grammar. Other e-rater variables (mechanics, word

length, and organization) were related to self-perceptions of writing and reading but not listening

and speaking. All correlations were fairly low, however, with the exception of vocabulary, which

had a correlation of .33 with self-perception of writing ability.

For the instructor ratings, the three ratings (writing, oral proficiency, and general English

ability) were combined into a single scale, composite evaluation of English ability. Because of the

differences between English and subject area instructors (discussed above), the correlations

between this variable and the averaged e-rater feature scores were computed separately (see Table

18). These correlations show a different pattern from the self-evaluation variables above, although

again the correlations are generally low. The usage and style variables were the only ones

statistically significant for both groups of instructors. Grammar and organization were correlated

with instructor evaluations for subject area instructors but not English instructors, and the other

four variables were significantly, although only slightly, related to the English instructors’

evaluations but not to the subject area instructors' evaluations.

As for correlations of e-rater feature scores and scores on course-related writing samples,

the correlations in Table 19 are generally higher than those from the student and instructor

surveys, particularly in the area of language. Vocabulary, grammar, and style have the highest

correlations with these scores, with correlations above .30 for the language scores.

Discussion

In summary, the e-rater features that are consistently related to criterion variables tend to

be those features related to linguistic accuracy (grammar, usage, style, and mechanics) rather than

those related to discourse (organization and development). The highest correlations overall tended

to be the correlations between these linguistic features as measured by e-rater and the ratings on

the language scale of submitted writing samples. This result in particular lends support to the

claim that the features evaluated by e-rater are similar to those used by human raters in judging

the effective use of language.

One intriguing result is that vocabulary level was highly related to student self-evaluations

and to ratings of writing samples, but not to instructor evaluations of language ability. This suggests

that instructors may focus on language errors more than other factors when asked to evaluate their

students’ language abilities in the abstract, whereas raters looking at specific writing samples take

into account a wider variety of features of writing when giving scores.

Research Question 3: Results and Discussion

Results

To answer Research Question 3 (regarding consistency of scores across prompts), human

and e-rater scores, as well as e-rater feature scores, were compared across the two writing

prompts. Table 20 shows the descriptive statistics for the averaged human rater scores and e-rater

scores for the two prompts. Recall that approximately half of the samples were rated by a group

of four raters from ETS and the other half were rated by two raters at GSU; as noted previously,

ratings were randomly assigned to either Rater 1 or Rater 2. Paired t-tests revealed that there were

no significant differences across prompts for any rater.

To further investigate the effects of both prompt and rater on scores, a two-factor

repeated-measures ANOVA was conducted with rater (Rater 1, Rater 2, e-rater) and topic (1 vs.

2) as independent variables. Results of this analysis are found in Table 21. As the table shows, the

analysis revealed a main effect for Rater but no overall effect for Topic or Rater/Topic interaction.

The rater effect can be seen in Figure 1: E-rater scores are lower than either of the two human

ratings. However, the effect is small (partial eta

= .03).

Table 20

Descriptive Statistics and Paired t-Tests for Human and e-rater Raters Across Prompts

Mean

T1 average human score

376

3.36

0.81

-.75

.45

T2 average human score

376

3.38

0.89

T1 e-rater score

377

3.24

0.91

-.24

.79

T2 e-rater score

377

3.24

0.94

Note. t-test results refer to comparisons of ratings by human raters and e-rater, respectively, across

the two prompts

Table 21

Repeated-Measures ANOVA for Topic and Rater on Scores Using Individual Raters

Source

Type III SS

Sig.

Partial eta

Topic

0.33

1.21

.27

.003

Error (Topic)

102.91

375

0.27

Rater

8.51

4.25

11.53

.00

.030

Error (Rater)

276.51

750

0.37

Topic * Rater

0.19

0.09

0.36

.70

.001

Error (Topic * Rater)

196.16

750

0.26

Figure 1. Plot of rater-by-topic interaction (single human raters vs. e-rater).

Table 22 shows the descriptive statistics, correlations, and results of paired t-tests of the e-

rater features between the two topics. The features are presented in descending order of Pearson

correlations between topics. As the table shows, the feature scores differed across topics in several

ways. The mean scores were significantly different across topics for all features except grammar

and organization, with three features (word length, vocabulary, and style) having higher means on

Topic 1 and others (development, mechanics, and usage) having higher means on Topic 2

(significantly higher mean scores are in boldface in the table). The features that were most

strongly correlated across topics were mechanics (.71), vocabulary (.63), and organization (.60),

with the lowest correlations for usage (.33) and grammar (.34).

Table 22

Descriptive Statistics, Correlations, and Results of Paired t-Tests Across Prompts of e-rater

Features (N = 377)

Topic 1

Topic 2

Cohen’s

Mean

Mechanics

.71

-0.16

0.07

-0.13

0.07

-9.01

.00

-0.56

Vocabulary

.63

-62.25

2.92

-64.41

2.55

17.66

.00

0.92

Organization

.60

1.92

0.37

1.88

0.37

1.75

.08

0.12

Development

.56

3.71

0.36

3.84

0.37

-7.45

.00

-0.38

Word Length

.49

4.59

0.24

4.23

0.24

28.31

.00

1.49

Style

.44

-0.21

0.13

-0.25

0.13

5.16

.00

0.29

Grammar

.34

-0.10

0.03

-0.09

0.03

-1.19

.24

-0.06

Usage

.33

-0.10

0.04

-0.09

0.04

-2.49

.01

- 0.21

Note. Significantly higher mean scores are in boldface.

Discussion

One criticism frequently leveled at large-scale writing testing programs is that decisions

are often made on the basis of a single essay written on a single topic. ETS has responded to such

criticisms in TOEFL iBT by including two different writing tasks (one independent and one

integrated); however, test takers still only respond to a single independent writing task, and thus it

is important to demonstrate that the specific prompt given to the test taker does not affect the

outcome of the test. The last research question addresses this issue.

The results of this analysis show that e-rater is generally more consistent across the two

prompts than individual human raters. This consistency is a potentially powerful argument in

favor of using e-rater operationally to eliminate or reduce human bias related to different writing

prompts. However, in practice differences in individual raters are mitigated by double-scoring and

using a large number of different raters, so that no individual rater bias has a serious detrimental

effect on test results. In this study rater bias may have been magnified by the use of a small

number of human raters who were trained but not highly experienced in rating TOEFL essays.

Human raters tended to rate essays slightly higher than e-rater did, but the effect size was small

and of little practical significance; furthermore, this result may have been due to the specific raters

used for the study.

However, even though the overall e-rater scores were consistent across the two topics, the

specific e-rater feature scores did not show the same consistency. There were significant

differences in six of the eight feature scores across the two topics; the largest differences were

found in the vocabulary and word length scores, with higher scores on Topic 1 (importance of

appearance) than Topic 2 (planning for the future). At the same time the vocabulary score was

highly correlated across the two topics, which indicates that the prompts rank-ordered students in

terms of their vocabulary usage in similar ways. An analysis of the 50 most frequently used words

among the student responses reveals that more words of over six letters were used frequently for

Topic 1 (appearance, fashion, personal, emphasis, and important) than for Topic 2 (planning and

carefully); since several of these words appear in the respective prompts, the differences in

vocabulary and word length scores may simply be an artifact of the prompt wording.

The differences in e-rater feature scores could be explored more thoroughly through a

textual analysis of TOEFL essays. For example, it may be the case that Topic 2 elicited more

personal narratives (and thus more instances of past tense) in support of arguments, while Topic 1

elicited supporting arguments in the form of general truth statements in the present tense,

resulting in a lower correlation between the grammar scores on the two topics (see Biber, 1988,

for a more complete discussion of the relationship between discourse genres and specific

linguistic structures).

The data presented here are also similar to data presented in Attali and Burstein (2006),

who found the true-score correlation between human and machine scores to be .97, a result

replicated in this study. On the other hand the alternate-forms reliability of individual e-rater

features presented by Attali and Burstein from essays written by 6

through 12

graders differs

from the reliabilities found in this study (see Table 23). In particular the mechanics reliability

coefficient in the current study is quite a bit higher than the same coefficient in the Attali and

Burstein study, while the grammar and usage reliabilities are lower. The range of reliabilities is

also greater. These differences may be due to the fact that different populations were used in the

two studies—Attali and Burstein’s data came from middle and secondary students, including both

native and nonnative speakers, while this study’s data came from university students, all of whom

are nonnative speakers of English.

Table 23

Comparison of e-rater Feature Correlations Across Alternate Forms of Writing Tests

Feature

Present study

Attali & Burstein (2006)

Mechanics

.71

.46

Vocabulary .63 .44

Organization

.60

.48

Development

.56

.36

Word Length

.49

.47

Style .44 .43

Grammar .34 .45

Usage

.33

.45

Implications and Future Directions

This study adds to the growing literature related to the validation and use of e-rater for

TOEFL essays (e.g., Attali, 2007, 2008; Attali & Burstein, 2006; Chodorow & Burstein, 2004;

Enright & Quinlan, 2008; Lee et al., 2008). In terms of the validity argument for the TOEFL

outlined by Chapelle et al. (2008), the study provides evidence that support the inferences of

generalization (across tasks and raters) and extrapolation to other criteria of writing ability in

academic contexts. As for the inference of utilization--that is, the inference that test scores

obtained in part through the use of e-rater are clearly interpretable and that the test will have a

positive influence on English teaching—perhaps one of the most serious deterrents to the

operational use of automated scores in high-stakes assessment is the opposition to machine

scoring by groups of stakeholders, especially teachers of writing and perhaps students themselves.

Evidence of the comparability of e-rater and human scores in terms of how they relate to nontest

indicators of writing ability may promote acceptance of automated scoring, especially if it can be

argued that automated scoring would help to contain the costs and resources needed to score the

test and mitigate the need to pass additional costs on to test takers, thus potentially increasing

access to the test. It may be that stakeholders are more willing to accept automated scoring of

second-language proficiency tests than of tests intended primarily for first-language writers, since

quantifiable sentence-level aspects of texts are more intuitively related to language proficiency

than are concerns of voice, audience awareness, and the ability to make a persuasive argument,

notions that are of paramount importance to composition teachers.

At the same time the study results suggest that e-rater cannot duplicate human ratings, and

there are still some differences between e-rater and human scores. This may even be a comforting

observation to writing teachers, since it emphasizes the fact that writing is primarily a means of

communicating between people, not a collection of measurable features of text. Presenting e-rater

as a tool to help streamline the process of making decisions on the basis of test scores rather than

as a substitute for human judgment may help allay the fears of those who object to machine

scoring of writing.

There are a number of limitations to this study, and a few of them will be mentioned here.

A number of sources of unreliability may have affected the correlations presented. The human

ratings on TOEFL essays may have been affected by the fact that the raters hired by the author

were not as extensively trained or experienced as the ETS raters. The writing samples submitted

by students were also of such varied nature that consistent scoring may have been a problem. In

addition, those writing samples were written in very different contexts, and participants had

access to varying levels of assistance and feedback, so they are not all strictly products of the

individuals who turned them in. The instructors who agreed to participate in the study also were

not a random sample; students may have chosen those teachers they thought would give positive

reports about their writing ability, or instructors who had particularly strong feelings about their

students’ language proficiency may have been more willing to take the survey. All of these

variables may have affected the study results.

Nevertheless, the data collected for this study represent a wealth of information about the

relationship between writing test scores and the role of writing in student success, and additional

analyses tangential to the focus of this report may be carried out. One analysis that might shed

light on some of the differences across topics would be a many-faceted Rasch analysis using the

FACETS software (Linacre, 2010; see also Myford & Wolfe, 2003, 2004, for details of this

method of analysis), which can be used to estimate rater severity and task difficulty on the same

linear scale, allowing investigation of questions such as whether specific raters judge essays on

certain topics more severely than others. This analysis could provide more detailed information

about rater bias, and along with e-rater feature scores could complement recent research on the

factors that influence rater behavior (e.g., Eckes, 2008).

Other questions less directly related to the focus of this report concern the relationship

between students’ perceptions of their abilities and their chosen field (do students at the same

level of proficiency but in different areas of study, such as science vs. business, perceive their

language needs differently?) or between a student’s self-assessment of his or her own ability and

the perceptions of that student’s instructor. The corpus of writing samples collected for this study

could also be useful in exploring questions about the role of writing in academic life. For

example, information was gathered on the types of help and feedback students received on the

writing samples they submitted; these data have not yet been analyzed and might shed light on the

academic lives of nonnative speakers in terms of what resources and support they find useful in

their writing. The analysis of writing samples in terms of register, cognitive demands, and use of

sources, while ultimately not proving overly informative for the present study, may have

implications for the study of the types of writing that students need to do for their coursework and

may in fact be more useful for subsequent analyses not related specifically to the TOEFL.

In conclusion, the findings presented in this report highlight the complex nature of writing,

the relationships between writing on a test and writing in academia, and the use of different

testing and scoring methods to make judgments about students’ readiness to participate fully in

academic life. The use of automated scoring in conjunction with human scoring to make the

process of assessing writing more efficient and potentially more reliable is just one of many

factors that may affect the outcome of assessments. There is no single ideal testing format or

scoring procedure, but e-rater certainly holds promise as an additional tool in the language tester’s

toolkit.

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Notes

The SPSS syntax used for these calculations was retrieved on September 1, 2009, from

http://ssc.utexas.edu/consulting/answers/general/gen28.html

t-test results are shown for Sample 1 only; similar results were found for Sample 2.

List of Appendices

A. - Student Survey ................................................................................................................ 48

B. - Factor Analysis of Student Survey Variables .................................................................. 51

C. - Instructor Survey ............................................................................................................. 53

D. - Factor Analysis of Instructor Survey Variables .............................................................. 56

E. - Participant Information Form .......................................................................................... 58

F. - Samples of Submitted Student Writing ........................................................................... 60

G. - TOEFL Independent Writing Task Scoring Guide ......................................................... 63

Appendix A

Student Survey

Appendix B

Factor Analysis of Student Survey Variables

Table B1

Unrotated Factor Matrix

Factor 1

Factor 2

Factor 3

Understanding what the writing assignment requires me to do

.685

.037

.162

Organizing my ideas

.647

.261

.176

Expressing my ideas clearly

.712

.200

-.196

Using appropriate vocabulary related to the subject

.678

.144

-.456

Using appropriate general vocabulary

.700

.105

-.401

Using English grammar correctly

.587

.387

-.282

Knowing how to format my papers correctly

.621

.342

.041

Doing my best writing within the amount of time given

.548

.271

-.145

Writing on a computer

.371

.365

.151

Understanding my instructor's lectures in class

.654

-.499

.021

Understanding my classmates

.568

-.522

-.179

Participating in class discussions

.566

-.474

-.191

Understanding the course readings

.664

-.257

.073

Reading fast enough to keep up with the amount of reading required

.651

-.215

.026

Understanding the subject matter and concepts related to the subject

matter

.711

-.130

.191

Getting used to the way classes are taught in the U.S.

.572

-.038

.331

Knowing how to get help with coursework when I need it

.672

.052

.298

Organizing my time to get all my school work done

.530

.092

.514

Table B2

Rotated Factor Matrix

Appendix C

Instructor Survey

Appendix D

Factor Analysis of Instructor Survey Variables

Table D1

Unrotated Factor Matrix

Component

Understanding the course readings

.844

.309

Understanding the requirements of writing assignments

.878

.289

Organizing ideas in writing

.875

.255

Expressing ideas clearly in writing

.860

.267

Using appropriate vocabulary in writing

.880

.207

Using English grammar correctly in writing

.846

.224

Understanding lectures and directions in class

.864

.240

Participating in class discussions

.767

.165

Asking for help when necessary

.775

.317

Lack of familiarity with the university culture

.226

.765

Lack of appropriate background for the course

.257

.866

Difficulties understanding concepts related to the subject matter

.249

.827

Inadequate study skills

.237

.831

Other nonlinguistic factors

.231

.826

Table D2

Rotated Factor Matrix

Appendix E

Participant Information Form

Appendix F

Samples of Submitted Student Writing

Figure F1. Sample 1.

Figure F2. Sample 2.

Figure F3. Sample 3.

Figure F4. Sample 4.

Appendix G

TOEFL Independent Writing Task Scoring Guide

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