Web-based Writing Assessment to Enhance Students’ English

Writing Performance

Erikson Togatorop

School of Education University of Newcastle Newcastle, Australia

Politeknik Negeri Batam, Indonesia

Keywords: Writing, English Writing, Writing Assessment, English Writing Assessment, Writing Performance,

English Writing Performance.

Abstract: Difficulties with English writing are a common cause of low English writing performance particularly for the

students who learn English as a second or a foreign language. This has been long become the concern of the

English educators and researchers, but not yet address how to facilitate the teachers or lecturers to do the

writing assessment easier and faster so that the students may get feedback promptly which is very important

for them to get progress in their writing learning and improve their writing performance. This study tried to

apply a web-based writing assessment and attempted to examine its affectivity to enhance the student writing

performance by employing a quantitative method in quasi-experiment design in the State Polytechnic of

Batam (Polibatam), Indonesia. It found that the web-based writing assessment as the treatment group was

better in enhancing students writing performance compared to the traditional conventional writing assessment

as the control group.

1 INTRODUCTION

English is a global language of communication and

writing is essential for students, both during their

study and after they graduate. Most student

assignments are written, so improving their writing

skills may give students a better chance to show what

they have learnt, whether they submit on paper or

through electronic media (Freiberg, 2008). When

students finish their study and move to the workforce,

writing will remain one of the determining factors of

a successful career. Inadequate writing proficiency

has provided serious difficulties for some university

graduates and produced strain in doing work related

to writing skills (Bonwell & Eison, 1991).

Writing is indeed a complex process that

encompasses invention of ideas and clear expression

of them in well- organized statements and paragraphs

(Nunan, 2003). This requires consolidation of

cognitive effort, attention control, and self- regulation

(Graham & Harris, 2003) and this makes writing

difficult to master, even for first language (L1)

learners (Nacira, 2010). Writing difficulties may

include lack of knowledge about the topic to be

written, lack of strategy in planning the text and lack

of proficiency in producing or revising it (Graham &

Harris, 2003). Second language (L2) writing learners

have more difficulty since they may find that words

are different when spoken and written and languages

differ in grammar.

Writing is important but it causes difficulties not

only for students who learn to write but also for the

teachers or lecturers who assess their pieces of

writing. Some researchers reported emerging

problems due to the teachers’ incompetence in

assessing writing (Edwards, 2012; Lee, 2008, 2009;

Montgomery & Baker, 2007) and even if the teachers

or lecturers are able to do the writing assessment well,

it does still take time. This long writing assessment

turnaround means that students frequently receive

late feedback. Students who receive late feedback are

often disappointed and find it more difficult to

improve their writing. Indonesian teachers and

lecturers face many large classes. The complex

nature of writing needs extra time and the large

classes in Indonesia make checking and marking

student writing difficult. This situation is common for

most Asian and some other developing countries

(Exley & Dennick, 2004) and often leads to low

quality, delayed and sometimes non-existent

feedback on assessment of student writing (Chang,

2007). Problems in giving prompt feedback and the

366

Togatorop, E.

Web-based Writing Assessment to Enhance Students’ English Writing Performance.

DOI: 10.5220/0010357903660373

In Proceedings of the 2nd International Conference on Applied Economics and Social Science (ICAESS 2020) - Shaping a Better Future Through Sustainable Technology, pages 366-373

ISBN: 978-989-758-517-3

complexity of the writing (Brown, 2001; Harmer,

2007; Nunan, 1999) both increase student

dissatisfaction, leading to low writing performance

(Nemati, Alavi, Mohebbi, & Masjedlou, 2017). There

is an emerging need of a more effective way that

could help teachers and lecturers to assess students’

writing and to give them prompt feedback to improve

their writing performance.

This research then tried to evaluate the use of a

web-based support for lecturers to do a faster

assessment on students writing tasks. The provision

of the web-based writing assessment was expected to

enable the lecturer to reduce the turnaround time in

assessing the students’ abundant pieces of writing.

This allowed the students to get prompt feedback on

their writing work hence was expected to increase

their writing performance.

2 LITERATURE REVIEW

Writing is a complex but very essential skills needed

by the students. This proficiency has a vital role in

the academic progress and success of a student as it is

still one of the main learning practice and assessment

especially in the tertiary schools (Chang, 2007).

Writing difficulties are usually associated with its

complicated components such as the development of

ideas, syntax, grammar, organization, vocabulary,

content, communication skills, and the use of

punctuation (Brown, 2001; Harmer, 2007). These

complexities make writing skill difficult to acquire

and frequently bring students to a level of

discouragement. These issues then have long become

the concern of English educators and researchers. A

long series of research literature has tried to find the

solution including the effort to apprehend the nature

of the writing itself to formulate a definition of

writing performance, English writing performance

both in L1 and L2, writing assessment, feedback in

writing assessment and the use of technology in

writing assessment.

A. Writing Performance

The effort to find the solution for the writing

difficulty should be started from understanding the

nature of writing itself in both in L1 and L2 context.

Nunan (2003, p. 88) defines writing as “the mental

work of inventing ideas, thinking about how to

express them, and organizing them into statements

and paragraphs that will be clear to the reader.” Other

experts stress the process it takes. Oshima and Hogue

(2007) for example defines writing as a repeated

process i.e. revising and rewriting. It was initially

accepted that the L1 writing approach could be used

as the “starting point” for L2 writing.

Further research though has been conducted to

formulate a more comprehensive definition of L2

writing performance. One of the most central research

objects, in this notion, is the position of linguistic

competence in L1 and L2 writing performance. L2

learner writers commonly still struggle with

grammatically correct sentences building in

accordance with their level of language proficiency.

Whereas the L1 learner writers with different levels

of linguistic competence, are at least familiar with

linguistic features, so that they have no substantial

problem with grammatical sentence forms. The

researchers in this line could conveniently define the

L1 writing performance as “a writer’s creativity,

logic, voice, style, success at self-discovery, and skill

at knowledge transforming” (Gennaro, 2006, p. 11).

The same competencies are embedded to the

definition L2 writing performance with the addition

of some more essential elements such as “L2

linguistic proficiency, balance between linguistic and

rhetorical sophistication (organization, coherence,

development) and task demands” (Gennaro, 2006, p.

11). In more detail, those L2 writing variables

competencies are divided into the discourse

competencies and the language competencies. The

discourse competencies cover the “organization,

coherence, progression, development of ideas, and,

depending on task, the ability to integrate or

summarize sources” (Gennaro, 2006, p. 11). The

language competencies, on the other hand,

encompass “vocabulary, illocutionary markers,

morphosyntax, spelling, and punctuation” (Gennaro,

2006, p. 11). The more emphasis on the role of the

linguistic features gives the distinction of

competencies of the L2 writing compared to the ones

of the L1 writing and will be also a consideration of

the L2 writing performance assessment.

B. Writing Assessment

Assessment is “a process for documenting, in

measurable terms, the knowledge, skills, attitudes,

and the beliefs of the learner” (Capraro et al., 2012, p.

1). In the context of wring assessment, there are two

emerging methods of assessing writing ability i.e. the

direct and the indirect writing assessment (Weigle,

2002), as can be seen in the table below.

Web-based Writing Assessment to Enhance Students’ English Writing Performance

367

Table 1. Writing Assessment Method

Writing

Assessment

Method

Treatment

1. Direct Method

equi

es writing at least one

sample writing assessing the

writing competence as a

whole

a. Holistic

gives a single score to all

elements of a piece of writing

based on an overall or a general

impression of the rater

b. Analytic

gives different separated score

for each element of the rating.

c. Primary Trait

concentrates of a certain element

as a prime trait but may still have

a secondary trait with a less

percentage

d. Weighting

Trait

weighting more on elements

2. Indirect

measures different features

of language competence

separately such as grammar or

vocabulary typically in form

multiple choice test

The difference of direct and indirect writing

assessment can be clearly seen in the table above. The

Direct Writing Assessment requires the examinee to

produce an actual writing that could be assessed

either by Holistic, Analytic, Primary Trait and

Weighting Trait Methods. The Holistic Scoring is

based on some criterion, but the rater does not score

on individual criteria. The Education Testing Service

(ETS) is one example of an organization that applies

this Holistic Writing Scoring. The Analytic, on the

other hand, gives individual scores on some different

elements of writing. For example, it may score five

different elements of content, organization,

vocabulary, language use and mechanics which each

will get 20 percent. The Weighted Trait Scoring is

basically the same with the Analytical one but

weighting more on elements. It may for instance give

more percentage on content or other element of the

writing. When a particular writing element is given

much more percentage than the others, it is then a

Weighted Trait Assessment Method. Indirect writing

assessment, on the other hand, does not require an

actual writing. The test taker’s competency will be

assessed on their ability on “the appropriate use of

language in a series of objective test items which

often follow a multiple-choice format” (Stiggins,

1982, p. 102). Those analyses above have been used

many times, for example by Boring (2005) in

comparing the validity of human and computerized

writing assessment, by Jayamani, Heng, and Bakri

(2016) in assessing Manufacturing Subjects, by

Ounis (2017) in assessing Speaking Course and by

Swain and Friedrich (2018) in assessing Writing

Course.

The L1 writing assessment can commonly be used

to the L2 assessment with more emphasis on the

linguistic elements. These elements in more detail are

called linguistic micro features divided into three

features i.e. lexical, synthetic and cohesion. In lexical

feature, the better scored L2 writings are the ones

with more words and with the words of more letters

or syllables and present greater lexical variety. In the

synthetic feature on the other hand, good L2 writings

are expected to present more subordinations and

passive voice uses. While in the cohesion features, the

L2 writers are supposed to use more conjunctions

(Crossley, Kyle, Allen, Guo, & McNamara, 2014).

Even though the L2 writing assessment gives

more emphasis on linguistic features than the L1 one

does, it does not necessarily mean that these features

become the most important. These items are easily

scored but it would not be hard to generate gibberish

that is well scored well on these criteria. As these

features are well known, many schools and language

training centers in Asia have provided English

international test preparation programs that only

focus on test strategy skills. Even though some may

achieve success this way, the writing assessment must

be kept in line with the learning purpose. How well a

test taker response to a writing task by providing

related points and relevant information should

become the main consideration of the assessment.

Good writing should also address “for what” and “for

whom” the writing is constructed. The content then

should be more important than the linguistic features

(Leki, 2004).

C. Feedback in Writing Assessment

Besides scoring, giving feedback is supposed to be an

essential integral part of the writing assessment. The

Assessments for Learning (AfL) theory, as can be

seen in the Diagram 1 below, particularly mentions

that it is the feedback that keeps clearly telling the

students “where they are now, where they need to go

and how they can best get there” (Edwards, 2012, p.

21).

The feedback needs to be given promptly to help

the students know their writing position in the

learning goal: what level is the students writing now,

ICAESS 2020 - The International Conference on Applied Economics and Social Science

368

what improvements they need to do and how they can

do those improvements. Students then will get serious

difficulties to revise their writing without having

adequate and prompt feedback from their lecturer.

Even those that belong to autonomous students still

need assistance intervention from their lecturer in

revising their writing. The writing complexities that

need long turnaround time to assess though often

make lecturers get late in giving the feedback. Some

lecturers even tend to skip this important step,

expecting that the students will just become

autonomous by themselves (Edwards, 2012). In some

cases, the writing learning process even ended up in

the students’ submitting their writing assignments

since those submitted assignments were never

returned to them (Chang, 2007).

Figure 1. Diagram of The Significance of Feedback in

Writing Learning According to The Assessments for

Learning (Afl) Theory

The problem of giving feedback is still

multifaceted by the problem of big classes which is

still prevalent in most of Asian developing countries

(Exley & Dennick, 2004), includes Indonesia.

Lecturers then should face complex assessment of

abundant pieces of students writing task regularly.

This problem leads to a low-quality feedback, slow

feedback or even no feedback at all (Chang, 2007). It

enhances dissatisfaction, brings discouragement to

some students, and finally boils down to low writing

performance (Nemati et al., 2017).

D. The Use of Information Technology in Writing

Assessment

The use of the information technology has long

become an alternative in writing assessment,

including the practice of Automatic writing

Evaluation (AWE) programs. Research has done

comparisons between this kind of assessment and

traditional manual writing assessment. It shows that

this computer based automatic assessment is better in

terms of shortening the assessment turnaround time

and has increased the students’ motivation and

convenience in receiving such prompt feedback

(Zhang & Hyland, 2018). The computer-based

feedback was also found to have a positive correlation

with the student writing performance and with the

students’ perception about the feedback (Ebyary &

Windeatt, 2010). Other research though also found

that while automatic writing assessment does shorten

turnaround time, it is only effective at word and

sentence level assessment. It does not work well in

providing adequate feedback at the content level

(Warschauer & Grimes, 2008). Automatic marking

can lose the content importance, particularly when it

applies analytic scoring methods in L2 writing

assessment. The L2 analytic assessment gives

separate marks to each writing element and test wise

students may easily “game” the test. For example, a

test taker who is only good in vocabularies could get

high writing score by producing more varieties of

words but less in meaning. The automatic assessment

machine could give high score on the words or

sentences feature which are meaningless in context.

The Education Testing Service (ETS) choice of using

holistic instead of analytic method is probably to

avoid this faulty scoring happening. As the largest

language test that relies on the combination of

automatic machine and human raters, ETS anticipates

the students who are only good in vocabulary levels

to get high marks on writing.

Another alternative to assist lecturers in doing a

faster assessment and providing effective feedback is

web-based feedback involving both human assessors

and technology. The writing assessment is still done

by human but is facilitated and assisted by

technology. Research has also covered this area and

found it is supportive (Bikowski & Vithanage, 2016;

Lin, Liu, & Yuan, 2001; Togatorop, 2015). Most of

them though were at the level of web-based peer

collaborative feedback (which is often not qualified

enough), not on lecturer’s feedback to students’

writing. Further research on the effort of providing a

technology-based writing assessment that could help

lecturers with the assessment is required. This

research still allowed them to give prompt feedback

on students writing to increase their writing

performance.

3 METHODOLOGY

This research applied a quantitative method

(Creswell, 2012). It used a quasi-experiment design

since it was done in two intact class groups without

fully randomization to avoid the learning system

Web-based Writing Assessment to Enhance Students’ English Writing Performance

369

disruption in the research site (Creswell, 2012; Vogt,

2005).

A. Data Sources

The study was done in Politeknik Negeri Batam

(PoliBatam): The State Polytechnic of Batam,

Indonesia. The population of the research was the

students of about 3319 persons in the institution. The

research applied purposive sampling that will chose

about 57 students who enrolled for the English

Writing Course in two of most similar characteristic

classes in the odd semester of academic year

2019/2020. Those two classes are in semester I of the

Business Management Department. The 57

respondents are mostly female and are commonly

seventeen to twenty years old. Before entering the

PoliBatam, they have also got English lesson for

years in their Primary and High School.

B. Research Scheme and Design

The research provided and analyzed two

different treatments of writing assessment in two

different classes. The Web-based Writing

Assessment as the treatment group and the

Traditional Manual Writing Assessment as the

control group. The impact of each of both treatments

was tested writing performance as can be seen in the

research scheme diagram 2 below.

Figure 2. Diagram of The Research Scheme

The study employed a pretest-posttest design

(Creswell, 2012) as can be seen in the table below.

Table 2. Research Design

All student participants in the two classes was

firstly given writing pretest to map their initial writing

performance level in the beginning of the course

before having the treatments. The two sample classes

then received the two treatments, one class got one

treatment, chosen randomly. And by the end of the

treatment, the writing performance level of each

group was reassessed with a posttest to see their

writing ability scale after following the treatment.

The pretest and the posttest were then analyzed and

the gain of each group both writing performance level

was calculated and compared to see how each

assessment system impacted the samples’ writing

performance.

C. Research Instrument

The instrument to measure the samples’ writing

performance in this research is a form of short

argumentative essay (about 300 words length). The

writing performance measurement instrument for

both pretest and posttest use same kinds of writing

and similar level of difficulty. The topic was different

though considering that writing is a skill which is

going to progressively get better after a rehearsal

process.

D. Data Analysis

The pre- and the post-test of writing performance in

this research were scored using the same marking

system applied by the Polibatam to assess their

students’ writing which is analytical writing scoring

model. The study was authentically reflecting what is

happening in the research site. As the result showed a

difference, it should be a difference in a way that the

institution could recognize. The pre and post-tests

writing score data where be analyzed using SPSS

to conduct descriptive and inferential statistic tests

to the collected data.

4 DATA FINDING AND

DISCUSSION

The students writing performance got increased in

both the Traditional and the Web-based writing

assessment group as can be seen from the writing pre-

test, post-test and gain of both group in the following

table.

Table 3. Writing Performance Gain of Traditional and

Web-Based Writing Assessment

ICAESS 2020 - The International Conference on Applied Economics and Social Science

370

As can be seen in the table above, the post-test

means of both groups are higher than the pre-test ones

meaning that the students’ writing performance of

both groups at the end of the treatment is better than

one in the beginning. The following table below

shows whether the difference of the pre- and post- test

writing performance of the Traditional writing

assessment group is significant or not.

Table 4. Writing Performance Pre-and Post-Test Paired T

Test of The Traditional Assessment Group.

The Sig (2-Tailed) value of .000 which is smaller

than .05 in in the last column of the output table of the

paired t test above shows that the difference of writing

post- and pre-test in the Traditional writing

assessment group is statistically significant at the at

the probability level of .05. This seems to indicate that

there is a significant impact of Traditional writing

assessment treatment on students writing

performance. The result of the same test conducted to

the pre- and post- test writing performance of the

Web-based writing assessment group can be seen in

the table below.

Table 5. Writing Performance Pre- and Post-Test Paired T

Test of The Web-Based Assessment Group

The Sig (2-Tailed) value of .000 which is smaller

than .05 in the output table of the paired t test above

shows that the difference of writing post- and pre-

test in the Web-based writing assessment group is

also statistically significant at the at the probability

level of .05. This seems to indicate that there is a

significant impact of Web-based writing assessment

treatment on students writing performance.

Even though both the Traditional and the Web-

based writing assessment groups gave a significant

impact to the students writing performance, the

writing performance gain of both groups is different

as can be seen in on the very left column of the Table

III above. That difference can be more clearly seen in

the graph below.

Figure 3. Graph of The Writing Performance Gain of

Traditional and Web-Based Writing Assessment.

The graph above clearly shows that the writing

performance gain of the Web-based group is almost

twice as the once of the one of the Traditional groups.

To know whether this writing performance gain

difference is significant or not, an Independent

Sample Test was conducted, and the result is as

shown by the following table.

Table 6. Independent Sample Test of The Traditioanal And

the Web-Based Assessment Group Writng Performance

Gain

In the above Independent Sample Test output

table, Sig. of the Levene Test for Equality of Variance

is .115 which is >0.05. It means that the data variance

between the Traditional and the Web-based group is

homogeneous. The interpretation of the Independent

Sample Test output then will be based on the "Equal

variance assumed" in the table. The sig (2-tailed) of

the "equal variance assumed" is .000 which is < .05.

It can be concluded then that the difference of the

writing performance gain between Traditional and the

Web-based group is statistically significant at the

probability of .05.

The finding of the research is in line with previous

studies that found the use of the computer web-based

writing assessment enhanced the students writing

performance (Ebyary & Windeatt, 2010; Zhang &

Hyland, 2018). This study did find that not only the

web-based but also the traditional writing assessment

increased the students writing performance. The

writing performance gain resulted by the Web-based

Web-based Writing Assessment to Enhance Students’ English Writing Performance

371

writing assessment group though was much higher

than the one by the Traditional writing assessment

group. The web-based technology helped the lecturer

to do the writing assessment easier and faster. It

enabled the lecture to give prompt and more

comprehensive feedback on students writing

assignments. The students then could better recognize

the strength and the weaknesses of their writings and

do improvements on their writing mistakes. The

web-based writing assessment practice in this study

could deal with the classic obstacles of the traditional

writing assessment, such as the writing assessment

complexities and long turnaround time (Edwards,

2012), big classes (Exley & Dennick, 2004), also

slow and low quality feedback (Chang, 2007). This

way, the students perceived the writing feedback

more positively and got it more useful for their

writing performance development.

The use of web technology in this study facilitated

a more effective writing assessment, but it was still

the lecturer himself who did the assessment using the

web media. This hence could overcome the weakness

of the web-based collaborative writing practice which

was also found in other previous studies give a

positive impact to students writing performance yet

not very effective since the feedback was given by

student peers (Bikowski & Vithanage, 2016; Lin et

al., 2001; Togatorop, 2015) who were not qualified

enough in writing as lecturer was. In the same way,

the combination of the lecturer feedback and the use

of the web technology could cope with the

shortcoming of the computer Automatic Writing

Evaluation (AWE) which was found effective in

shortening the assessments time and in assessing the

students writing in words and sentence level (Zhang

& Hyland, 2018) but not in content level (Warschauer

& Grimes, 2008). The feedback given by this lecture

web- based writing assessment paid enough attention

to the student writing content as it is an essential part

of writing assessment (Leki, 2004). This way the

lecture web-based writing assessment feedback could

play its very central role to enable the students to

recognize the shortcomings of their writing, what

improvements they need to do and how to do it

(Edwards, 2012).

5 CONCLUSIONS

This study found that the Web-based writing

assessment is significantly better than the Traditional

conventional writing assessment. The writing

performance gain of the Web-based writing

assessment class as the treatment group is much

higher than the one of the Traditional conventional

writing assessment class as the control group. The

Independent Sample Test conducted to test the

writing performance gain difference of the treatment

and the control group shows that difference is

statistically significant at probability level of 0.05.

The practice of the lecturer web-based writing

assessment could cope with long turnaround

assessment time of the traditional writing assessment.

Regarding to the above conclusion, it is

recommended to continue applying and expanding

the use of the web-based writing assessment in

Polibatam. For further studies in this topic, it was

encouraged to do similar research with other different

variables on a bigger population.

ACKNOWLEDGMENT

I would like to acknowledge supports that the

Indonesian Endowment Fund for Education (LPDP)

and the Indonesian Ministry of Education and Culture

in making this research and presentation possible

under the Dikti Scholarship.

REFERENCES

Bikowski, D., & Vithanage, R. (2016). Effect of web based

collaboration writing on individual L2 writing

development. Language Learning & Technology,

20(1), 79-99.

Bonwell, C. C., & Eison, J. (1991). Higher Education

Report. Retrieved from http://www.active-learning-

site.com/

Boring, R. (2005). The validity of human and computerized

writing assessment. Paper presented at the Human

Factors and Ergonomics Society 49th Annual Meeting,

Orlando.

Brown, H. D. (2001). Teaching by principles: An

interactive approach to language pedagogy (2 ed.).

California: San Francisco State University.

Capraro, R. M., Roe, M. F., Caskey, M. M., Strahan, D.,

Bishop, P., Weiss, C., & Swanson, K. W. (2012).

Research summary: Assessment. Retrieved from

https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi

?article=1006&context=ci_fac

Chang, G. C. (2007). Writing feedback as an exclusionary

practice in higher education. International Journal of

Pedagogies and Learning, 3(2), 18-30.

Creswell, J. W. (2012). Educational research: Planning,

conducting, and evaluating quantitative and qualitative

research (4th ed.). Boston, MA: Pearson.

Crossley, S. A., Kyle, K., Allen, L. K., Guo, L., &

McNamara, D. S. (2014). Linguistic microfeatures to

predict L2 writing proficiency: A case study in

ICAESS 2020 - The International Conference on Applied Economics and Social Science

372

Automated Writing Evaluation. The Journal of Writing

Assessment, 7(1), 1-16.

Ebyary, K., & Windeatt, S. (2010). The impact of

computer-based feedback on students’ written work.

International Journal of English Studies, 10(2), 121-

142.

Edwards, E. (2012). Applying action research to investigate

the use of goal setting for EFL-ESL writing. English

Australia Jounal, 29(1), 19-38.

Exley, K., & Dennick, R. (2004). Giving a lecture - from

presenting to teaching. London: Routledge Falmer.

Freiberg, J. (2008). Criteria, standards and intuitions in the

imprecise work of assessing writing. Literacy Learning,

16(13), 20-26.

Gennaro, K. d. (2006). Second language writing ability:

Towards a complete construct definition. TESOL &

Applied Linguistics, 6(2).

Graham, S., & Harris, K. R. (2003). Students with learning

disabilities and the process of Writing. A meta-analysis

of SRSD studies. New York: Guilford.

Harmer, J. (2007). The practice of English language

teaching (4 ed.). Harlow: Longman.

Jayamani, E., Heng, S. K., & Bakri, M. K. (2016). Effect of

developing analytic/task specific rubric for an

enhanced student learning in manufacturing subjects.

Paper presented at the Annual International Conference

on Computer Science Education: Innovation &

Technology.

Lee, I. (2008). Understanding teachers' written feedback

practises in Hong Kong secondary classrooms. Journal

of Second Language Writing, 17(2), 69-85.

Lee, I. (2009). Ten mismatches between teachers' beliefs

and written feedback practice. ELT Journal, 63(1), 13-

22.

Leki, I. (2004). Teaching second-language writing: Where

we seem to be. In T. Kral (Ed.), Teacher Development

(pp. 170-178 ). Washington, DC: English Language

Programs Division of the USIA.

Lin, S. S. J., Liu, E. Z. F., & Yuan, S. M. (2001). Web-based

peer assessment: Feedback for students with various

thinking-styles. Journal of Computer Assisted

Learning, 17, 420-432.

Montgomery, J. L., & Baker, W. (2007). Teacher-written

feedback: Student perception, teacher self-assessment,

and actual teacher performance. Journal of Second

Language Writing, 16, 82-99. Retrieved from

https://doi.org/10.1016/j.jslw.2007.04.002

Nacira, G. (2010). Poor writing production: The case study

of 3rd year students at the English Department - Batna

University. (Master). University of Setif, Algeria.

Nemati, M., Alavi, S. M., Mohebbi, H., & Masjedlou, A. P.

(2017). Speaking out on behalf of the voiceless learners

- written corrective feedback for English language

learners in Iran. Issues in Educational Research, 27(4),

822-841.

Nunan, D. (1999). Second language teaching and learning.

Boston, MA: Heinle & Heinle.

Nunan, D. (2003). Practical English Language Teaching.

Singapore: Mc Graw-Hill Company.

Oshima, A., & Hogue, A. (2007).

Introduction to Academic

Writing (3 ed.). White Plains, NY: Longman.

Ounis, M. (2017). A comparison between holistic and

analytic assessment of speaking. Journal of Language

Teaching and Research, 8(4), 679-690.

Stiggins, R. J. (1982). A Comparison of Direct and Indirect

Writing Assessment Methods. Research in the

Teaching of English, 16(2), 101-114.

Swain, S. S., & Friedrich, L. (2018). A common language

and criteria to boost students' writing: an analytic

writing rubric helps teachers and students see what's

present and possible in a piece of student writing.

Educational Leadership, 75(7), 66-71.

Togatorop, E. (2015). Teaching writing with a web-based

collaborative learning. International Journal of

Economics and Financial Issues, 5(Special Issue), 247-

256.

Vogt, W. P. (2005). Dictionary of statistics and

methodology: A nontechnical guide for the social

sciences (3rd ed.). Thousand Oaks, CA: Sage.

Warschauer, M., & Grimes, D. (2008). Automated writing

assessment in the classroom. Pedagogies: An

International Journal, 3, 32-36.

Weigle, S. C. (2002). Assessing writing. Cambridge:

Cambridge University Press.

Zhang, Z. V., & Hyland, K. (2018). Student engagement

with teacher and automated feedback on L2 writing.

Assessing Writing, 36, 90-102.

Web-based Writing Assessment to Enhance Students’ English Writing Performance

373