





































Putri Anggraeni, et al / Journal of English Language Teaching 6 (1) (2017) 

36 

   

 
ELT FORUM 12(1) (2023) 

 

Journal of English Language Teaching 

 
http://journal.unnes.ac.id/sju/index.php/elt 

 

 

A comparability study of handwritten versus typed responses in high-stakes 

English language writing tests 

 

Irene Stoukou1, Yiannis Papargyris1, David Coniam1 
 
1PeopleCert, Athens, Greece 

 

Article Info 
________________ 
Article History: 
Received in 15 
February 2023 

Approved on 30 March 
2023 
Published on 31 March 
2023 

________________ 
Keywords: handwritten 
scripts; computer-
processed scripts; effect 
size; equivalence t-tests 
____________________ 

 

 

 

 

Abstract 
___________________________________________________________________ 
This paper investigates fairness in writing test scores in terms of candidates who 
completed a writing test either by hand or typed, on a computer. The data for this 

large-scale comparability study comprise candidates taking English language writing 
tests at four CEFR levels – B1 to C2 in the period 2019–2022. The data were 

analysed via effect size differences and equivalence tests. Measured by effect size, a 
small amount of difference was apparent in scores obtained between the two 

production modes at B1, B2 and C1 levels. At C2 level, there was a medium effect 

size, indicative of a difference in favour of computer-produced scripts. Differences 
observed on equivalence tests – an adaptation of the standard t-test – were not found 

to be statistically significant. The contribution of the research to knowledge lies in 
the fact that (with the exception of C2 level) – whether writing tests are written by 

hand or on computer, while there is a slight skew towards higher scores with 
computer-processed texts, candidates generally receive similar scores in both modes. 

Practically, candidates may elect to write either on paper or on computer without 

fear of bias. 

 

  
Correspondence Address: p-ISSN 2252-6706 | e-ISSN 2721-4532 

3 Korai Street, Athens 10564, Greece  

E-mail: david.coniam@peoplecert.org  

http://issn.pdii.lipi.go.id/issn.cgi?daftar&1333515478&1&&
http://issn.pdii.lipi.go.id/issn.cgi?daftar&1576658845&1&&


Irene Stoukou, Yiannis Papargyris, David Coniam | ELT Forum 12(1) (2023) 

37 

 

INTRODUCTION 

There is a substantial literature on score equivalence obtained from handwritten (HW) and 

computer-processed (CP) scripts. Indeed, research into score equivalence between handwritten and 

computer-processed scripts stretches back to the 1960s when the word-processing of scripts first 

began. To put these issues into perspective, the current section first presents an overview of the 

research – which presents provide contrasting results. The section concludes with the research gap 

being explored in the current research. 

While some studies have revealed better performance by candidates writing by hand; others 

have reported the opposite, with higher CP scores; and, in contrast, no significance has been found 

for either mode of delivery in other studies. A review of the research from the different angles is 

presented below. 

Some of the earliest research was by Marshall and Powers (1969), in whose study neat 

handwritten essays scored higher than typed ones. Mazzeo and Harvey’s (1988) study of 

handwritten and computer-processed scripts indicated better performance in HW mode, which they 

attributed, understandably at the time, to lack of familiarity with the technology.  

Arnold et al. (1990) reported computer-processed scripts receiving lower scores than 

handwritten scripts. Sweedler-Brown (1991) reported likewise, although only with lower ability 

scripts. In Powers et al.’s (1994) and Russell and Tao’s (2004) studies, students’ HW scripts scored 

higher than the same students’ comparable CP scripts. Bridgeman and Cooper (1998) in a study 

involving Graduate Management Admissions Test scores reported higher scores with HW than with 

CP scripts. Klein and Taub (2005) reported a teacher bias for legible HW scripts. In Breland et al.’s 

(2005) study of TOEFL candidates, HW scores, related to general English language ability, were 

reported. 

While numerous studies have reported handwriting-based scripts to have received higher 

scores, there have also been many studies reporting computer-processed-based scripts to have 

received higher scores. Some studies showing such advantage are outlined below. An overall 

advantage for CP texts has been reported in certain studies (Sprouse and Webb, 1994; Peacock, 

1988; Hughes and Akbar, 2010). On the issue of quality, Peacock (1988) reported an advantage for 

low-quality CP scripts. Peacock (1988) also reported an advantage regarding text type for CP essays 

where the essays were not related to external sources. In Canz et al.’s large-scale (2020) study, CP 

scripts received higher grades despite raters being highly trained raters. Russell and Plati (2000) 

reported lower secondary school students performing better under CP conditions. In Goldberg et 

al.’s (2003) meta-analysis of 26 writing studies of K-12 students writing in CP or HW modes, results 

indicated higher text quality for the CP scripts. Other confirmatory studies for students achieving 

higher grades in CP mode include Russell and Haney (1997) and Russell and Plati (2001). 

In addition to studies citing an advantage for either mode, here have also been studies where 

neither mode has been reported as conferring an advantage, as outlined below. While positive 

findings have been reported for both modes, a number of studies have reported no significant 

difference in terms of grade received in either CP or HW mode. Among these are: Wise and Plake, 

1989; Wright and Linacre, 1994; Taylor et al., 1999; Russell, 1999; MacCann et al., 2002; Horkay et 

al., 2006; Boulet et al, 2007; King et al., 2008; Mogey et al., 2010; Chan et al., 2018. As may be seen 

from the studies reported above, there is evidence for all positions: that under certain conditions CP 

scripts receive higher scores; under others that HW scripts score higher, with many studies also 

reporting no significant difference between modes. 

Differences notwithstanding, it is nonetheless the case that with improvements in technology 

in terms of usability, speed and lower cost (see Lim and Wang, 2016), the use of a computer to 

produce essays in a variety of situations – classwork, homework and examinations – is increasing. 

Indeed, with the recent COVID-19 pandemic, greater acceptance has been observed of the use of 

computers and technology (Hodges et al., 2020). 

In light of the above, it is worth considering the question of whether the ability or preference 

to use a computer in an examination is related to age. Older candidates do not necessarily opt for 

CB tests as such; it is simply the route they follow which leads them to an online-proctored 

environment (i.e., navigating the internet, selecting an exam provider online, registering, booking a 

slot and managing their time etc.). Against this backdrop, for more mature candidates, the CB 

component is simply part of the overall context.  

Over the past three years, that is, during the period of COVID in 2019–2022, many 

examination bodies experienced exponential increases in online-administered examinations (see 



Irene Stoukou, Yiannis Papargyris, David Coniam | ELT Forum 12(1) (2023) 

38 

 

e.g., Ockey, 2021). LanguageCert English language tests are available in either traditional centre-

based or online proctored (OLP) delivery modes (Coniam et al., 2021). During the COVID 

pandemic, LanguageCert saw a great increase in its OLP mode of delivery, and a concomitant 

increase in writing tests produced by computer as opposed to being handwritten. While the research 

outlined above has presented different perspectives on the two modes of delivery – computer-

processed versus handwritten – and how the mode might confer an advantage on scores, little 

research has been conducted in the past few years – and certainly not in the context of the huge 

increase in computer-processed writing test scripts against the backdrop of COVID.  

This is therefore the research gap that the current study fills in the context of computer-

processed versus handwriting writing test scripts. Using comparatively large writing test datasets (a 

considerable number of which had been administered during the COVID pandemic period) at 

differing CEFR levels of ability, the study explores to what extent the mode of script production 

impacts on candidate score. 

 

METHODS 

This section outlines the study in terms of research design, the data and data analysis. Background 

to the LanguageCert Writing Test is first presented to situate the study.  

 

The IESOL writing test 

The data in the study come from four examinations – at CEFR levels B1–C2, which form part of the 

International ESOL (IESOL) suite of English language tests. The Writing Tests comprise two 

different writing tasks tapping a range of writing skills. Table 1 elaborates. 

 

Table 1. IESOL writing test tasks 

Level  Part 1 : Candidates produce Word 

length 

Part 2 : Candidates produce Word 

length 

B1  a neutral or formal text for a 

public audience  

70-100 a letter using informal language  100-120 

B2  a neutral or formal text for a 

public audience  

100-150 a text using informal language  150-200 

C1  a neutral or formal text for a 

public audience  

150-200 a text using informal language  250-300 

C2  a neutral or formal text for a 

public audience  

200-250 a text using informal language  250-300 

 

Each task is scored on four levels (0-3) against four subscales which for the most part are 

double-marked before final scores are amended or confirmed and signed off by a more senior 

member of the assessment team, usually a chief examiner. (Refer to LanguageCert). 

Candidates may take the examination either at a physical centre or by online-proctored mode. 

If they take the examination at a centre, they generally handwrite. While it is possible to do a 

computer-based test at a physical centre, this option is not very popular; most candidates handwrite 

tests at centres. When tests are taken online, a locked-down computer is used. It should be noted 

that the term ‘computer-processed’ is used in the current paper to indicate that candidates write on a 

‘bare-bones’ computer; they do not have access to a word processor or any of the more advanced 

facilities such as grammar/spellchecking that a word processor offers.  

All Writing Test markers hold professional accredited English language qualifications and 

experience as English language teachers. All prospective markers undergo a standardisation and 

training programme before being certified as qualified markers (for details, see Papargyris and Yan, 

2022). The training programme involves marking sample scripts and prospective markers must 

demonstrate they can mark accurately and consistently before they are certificated as markers. 

Checking takes place by a group of chief examiners during live marking, and if markers are 

suspected of marking inaccurately and/or inconsistently, they may be removed from the marking 

session and/or retrained or even dismissed. Markers are monitored on an ongoing basis as well as 

attending standardisation sessions, again on a regular basis. LanguageCert markers mark across 

CEFR levels (Papargyris and Yan, 2022). At any one time, there may well be in the region of 200 

markers marking different numbers of scripts at the different CEFR levels. While the scope of the 

https://www.languagecert.org/en/language-exams/


Irene Stoukou, Yiannis Papargyris, David Coniam | ELT Forum 12(1) (2023) 

39 

 

current study does not involve an examination of marker performance, the reader is referred to 

Coniam et al. (2022) where an exploration using Many-Facet Rasch Analysis into marker 

performance can be found.  

 

The current study 

This section presents details on candidates’ scores against the two modes of script production. Table 

2 below provides detail on the number of candidates at each CEFR level for each mode. The data 

collection period extended over the three-year period from mid 2019 to mid 2022. Although not 

germane to the current study, it should be noted that the current study involved 143 different 

markers. 

 

Table 2. Candidate sample sizes 

Level Mode N Level sample 

B1 CP 3108 
22727 

 HW 19619 

B2 CP 14878 
27590 

 HW 12712 

C1 CP 7674 
10330 

 HW 2656 

C2 CP 2869 
4363 

 HW 1494 

Legend: CP=computer-processed; HW=Handwritten 

 

At B1 level, the candidature comprises many school students. It is therefore not perhaps 

surprising that the majority of scripts at this level were handwritten. As one moves up the level, and 

demands of certification for study, work, immigration purposes come more to the fore, candidates 

tend to be slightly older and more computer literate. More online-proctored (OLP) examinations 

take place at this level, a situation exacerbated by COVID, and support for why computer-processed 

(CP) scripts outnumber handwritten (HW) scripts at B2-C2.  

 

Hypotheses 

The hypothesis pursued in the study is that scores awarded to either of the two modes of script 

production – computer-processed or handwritten – will not be significantly different. Three sub-

hypotheses are pursued: 

1. The difference between the mean scores for the two written script modes will be less than 

5% for any given CEFR level.  

2. Only small effect size differences will be noted between the two modes. 

3. On equivalence tests, significance will not emerge against specified upper and lower 

bounds for any CEFR level.  

 

FINDINGS AND DISCUSSION 

Two sets of data for the Writing Test are presented. The first set of analyses contains descriptive 

statistics: means (maximum 25) for the two modes, standard deviations and effect size differences. 

The second set of analyses consists of equivalence independent samples t-tests (“equivalence tests”). 

Equivalence tests – as opposed to regular t-tests – permit for significance to be explored by specified 

upper and lower bounds (Lakens, 2017). The two bounds define the extent of variation of t values 

with respect to the populations being tested. If the t value falls within the estimated range, the two 

populations may be seen to be equivalent.  

 

Descriptive statistics 

Descriptive statistic results are in provided in Table 3 for the two types of writing for the four CEFR 

levels. The final two right-hand columns contain detail on score and effect size differences between 

the two modes. Effect size differences are reported in terms of Cohen’s d, for which a small effect is 

generally 0.2, a medium effect 0.5, and a large effect 0.8 (Glen, 2021). 

Table 3. Writing mode descriptives 



Irene Stoukou, Yiannis Papargyris, David Coniam | ELT Forum 12(1) (2023) 

40 

 

Level Mode N Mean SD 
Raw score (%) 

difference 

Effect size differences 

(Cohens's d) 

B1 
CP 3108 18.75 4.63 

0.80 (3.20%) 0.17 
HW 19619 17.95 4.72 

B2 
CP 14878 18.85 4.68 

0.80 (3.21%) 0.17 
HW 12712 18.04 4.67 

C1 
CP 7674 17.60 4.80 

1.13 (4.53%) 0.23 
HW 2656 16.46 4.86 

C2 
CP 2869 18.13 4.77 

2.57 (10.28%) 0.55 
HW 1494 15.56 4.46 

Legend: CP=computer-processed; HW=Handwritten 

 

Effect sizes reported are negligible for B1 and B2 levels, with only a small effect size at C1. At 

C2 level, however, the score difference is larger than 5%, and a medium effect size of 0.55 is 

reported. The implications of this are that C2 level candidates, who produce their Writing Test 

scripts on computer, score comparatively higher than C2 candidates who handwrite their tests.  

 

Equivalence tests 

Table 4 below presents equivalence test results comparing handwritten (HW) and computer-

processed (CP) script production modes. Upper and lower bounds have been set at +/- 0.05 of the 

raw score (see Lakens, 2017). As mentioned, critical decisions regarding equivalence revolve around 

whether estimated t values are between the upper and lower bound. In Table 4 below, p values 

indicate significance with respect to upper and lower bound t values going beyond specified bounds. 

 

Table 4. Equivalence samples t-tests 

Test Level Statistic t df p 

B1 
upper bound 9.36 22725 < .001 
t value 8.81 22725 < .001 

lower bound 8.26 22725 1.00 
 

B2 

upper bound 15.12 27588 1.00 

t value 14.23 27588 < .001 
lower bound 13.34 27588 < .001 

 

C1 
upper bound 9.99 10328 1.00 
t value 10.45 10328 < .001 

lower bound 10.91 10328 < .001 
 

C2 

upper bound 16.92 4361 1.00 

t value 17.26 4361 < .001 
lower bound 17.59 4361 < .001 

 

At none of the four levels was significance observed at both lower and upper bounds. This is 

an indication that the two writing script modes may be seen to be equivalent for the four CEFR 

levels examined in the study. 

 

Discussion 

The results above provide a consistent picture: at all levels, candidates who produced computer-

processed scripts scored higher than did candidates who produced handwritten scripts. This finding 

echoes the study by Goldberg et al. (2003) who analysed studies of students writing in CP or HW 

modes, with results indicating CP scripts being rated more highly. As Lim and Wang (2016) report, 

the use of a computer to produce essays in many school situations is increasing. It may simply be the 

case that such increasing use of the computer results in a vicious, or virtual, cycle (depending on 

one’s point of view), whereby writing on computer becomes the norm and the mode to which 

people, examination candidates included, are simply becoming more accustomed.  



Irene Stoukou, Yiannis Papargyris, David Coniam | ELT Forum 12(1) (2023) 

41 

 

The results for higher scores obtained on computers may be due to a number of factors. One 

consistent feature mentioned by LanguageCert markers in post-marking reports is that of the 

legibility (or lack of it) encountered in many handwritten scripts. Be that as it may, the main issue is 

that at CEFR levels B1-C1, the difference between the two modes is less than 5%, a figure generally 

taken as being indicative of significance.  

What then might be the possible reasons for candidates using a computer to produce their 

script – in particular at the higher CEFR levels – to obtain comparatively higher scores? One possible 

explanation may be found in the candidates’ background. In a survey (in mid-2022) of over 40 

LanguageCert Writing Test markers, markers noted that, at the CEFR A and B levels, there were 

more younger candidates. These younger candidates were more used to writing on paper than using 

a computer. More proficient candidates – in particular those at C2 – were noted by some markers as 

being older and more computer literate. Markers perceived these two factors as helping to account 

for the skew towards higher scores achieved on computer-processed scripts.  

 

CONCLUSION 

This study has reported on comparability of scores awarded to candidates who completed Writing 

Tests either through handwriting or by using a computer at CEFR levels B1 to C2. 

The key hypothesis in the study was that mean scores and performance on the Writing Test in 

either mode would not be significantly different from each other; i.e., that candidate scores would 

not be influenced by the writing mode. Specifically, three hypotheses were being investigated. 

The first hypothesis was that differences between the mean scores for the two modes of test 

production would be less than 5% for any given CEFR level. This was the case for levels B1, B2 and 

C1. It was not the case for C2 where differences were greater than 5%. While the hypothesis was 

confirmed for B1, B2 and C1, it was rejected for C2. 

The second hypothesis was that, at worst, only small effect sizes would be reported between 

the two writing modes. This was indeed the case with B1, B2 and C1. At the C2 level, however, a 

medium effect size was observed, causing the hypothesis to be rejected. 

The third hypothesis was that, for any given CEFR level, significance between upper and 

lower bounds would not be observed on equivalence t-tests. Significance was not observed for either 

bound at any test level. Consequently, the two script writing modes can be taken as broadly 

equivalent, and the hypothesis can be accepted. 

While differences at B1 and B2 were minimal, it could be seen that as one moved up the 

CEFR levels, the relative score gain conferred by using a computer increased. At B1 and B2 the 

difference was 3%. At C1, it was 5%, and at C2, 10%. 

As mentioned above, use of a computer in an examination may be seen to be related to age in 

that older candidates simply follow an online path which leads to an online-proctored environment 

(i.e., navigating the internet, selecting an exam provider online, registering, booking a slot and 

managing their time etc.). For older candidates, the CP component in terms of how a test is taken 

may well be seen as simply a part of an online path they have followed.  

The current study has been purely quantitative. A further study is currently exploring Writing 

Test markers’ views on the effect of certain linguistic or textual features on candidates’ scripts. 

Echoing markers’ comments alluded to above, a more fine-grained examination lies in determining 

to what extent demographic factors such as age might have an effect on results obtained from 

writing tests by hand versus on computer.  

Another aspect of the interaction between digital environment and textual production, worth 

exploring in the future, is that of task requirements vis-a-vis the support each environment allows. In 

a digital environment for instance, candidates have the option of employing a variety of content 

control features (provided these are made available by the test provider). Such features may 

significantly contribute to the authoring, editing, and proofreading of longer, complex and 

structurally challenging texts and thus account for the increasing discrepancy between scores, which 

culminates at C2.  

The research literature revealed support for all modes: for handwritten scripts, for scripts 

written on computer, and for there being no difference. The current study, however, lends support to 

the view that, while differences remain, it is computer-processed scripts that certain candidates tend 

to score higher on.  

A generally greater uptake of the use of computers is seen in the production of text – for all 

purposes, not just examinations. In the light of such uptake, one potential solution to the 



Irene Stoukou, Yiannis Papargyris, David Coniam | ELT Forum 12(1) (2023) 

42 

 

discrepancy score situation, as one looks to the future, is that all scripts be computer processed. 

Indeed, many professional examinations – law examinations, for example (Steel et al., 2019) – are 

now required to be done solely on computer as are the Association of Chartered Certified 

Accountants’ (ACCA) financial and accounting examinations.  

The COVID pandemic has accelerated the computer processing of scripts, with many more 

candidates taking exams online rather than on paper (Fuller et al., 2020; Abduh, 2021). For such a 

move to be accepted more widely, however, school students in particular need to have easy access to 

a computer and to be computer literate. This is contingent upon schools moving increasingly 

towards total computer-based work, with each child having their own laptop for continual school 

and home use, as with Uruguay’s Plan Ceibal (see Segovia et al., 2022), for example. In the UK, the 

government Office of Qualifications and Examinations Regulation (Ofqual) has recently announced 

a three-year plan to explore the possibility of across-the-board online testing for students (Ofqual, 

2022). Indeed, in the long run, what Mogey and Fluck (2015) describe as “post-paper assessment” is 

possibly what education and assessment authorities should be considering. Whether these changes 

will happen quickly will be observed and reported on in due course. 

 

FUNDING STATEMENT 

The authors received no financial support for the research and/or authorship of this article. 

 

REFERENCES 

Abduh, M. Y. M. (2021). Full-time online assessment during COVID-19 lockdown: EFL teachers’ 

perceptions. Asian EFL Journal, 28(1.1), 26-46. 

Association of Chartered Certified Accountants Association. Computer-based exams. (n.d.). 

https://www.accaglobal.com/vn/en/student/exam-support-resources/fundamentals-exams-

study-resources/f1/technical-articles/computer-based-exams.html. 

Arnold, V. (1990). Do students get higher scores on their word-processed papers? A study of bias in 

scoring hand-written vs. word-processed papers. The Educational Resources Center. Whitter, 

CA Rio Hondo College. 

Boulet, J. R., McKinley, D. W., Rebbecchi, T., & Whelan, G. P. (2007). Does composition medium 

affect the psychometric properties of scores on an exercise designed to assess written medical 

communication skills?. Advances in Health Sciences Education, 12(2), 157-167. 

Breland, H., Lee, Y. W., & Muraki, E. (2005). Comparability of TOEFL CBT essay prompts: 

response-mode analyses. Educational and Psychological Measurement, 65(4), 577-595. 

Bridgeman, B., & Cooper, P. (1998). Comparability of scores on word-processed and handwritten 

essays on the Graduate Management Admissions Test. http://eric.ed.gov/?id=ED421528. 

Canz, T., Hoffmann, L., & Kania, R. (2020). Presentation-mode effects in large-scale writing 

assessments. Assessing Writing, 45, 100470. 

Chan, S., Bax, S., & Weir, C. (2018). Researching the comparability of paper-based and computer-

based delivery in a high-stakes writing test. Assessing Writing, 36, 32–48. 

https://doi.org/10.1016/j.asw.2018.03.008. 

Coniam, D., Lampropoulou, L., & Cheilari, A. (2021). Online proctoring of high-stakes 

examinations: A survey of past candidates' attitudes and perceptions. English Language Teaching, 

14(8), 58-72. https://doi.org/10.5539/elt.v14n8p58. 

Coniam, D., Stoukou, I., Lee,. T., & Milanovic, M. (2023). SELT IESOL Writing Test quality. 

London, UK: LanguageCert.  

Fuller, R., Joynes, V., Cooper, J., Boursicot, K., & Roberts, T. (2020). Could COVID-19 be our 

‘There is no alternative’(TINA) opportunity to enhance assessment?. Medical Teacher, 42(7), 

781-786. 

Goldberg, A., Russell, M., & Cook, A. (2003). The effect of computers on student writing: A meta-

analysis of studies from 1992 to 2002. Journal of Technology, Learning, and Assessment, 2(1).  

Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2020).The difference between emergency 

remote teaching and online learning. EDUCAUSE Review. 

https://er.educause.edu/articles/2020/3/the-difference-between-emergency-remote-teaching-

and-online-learning. 

Horkay, N., Bennett, R. E., Allen, N., Kaplan, B., & Yan, F. (2006). Does it matter if I take my 

writing test on computer? An empirical study of mode effects in NAEP. Journal of Technology, 

Learning, and Assessment, 5(2). https://ejournals.bc.edu/index.php/jtla/article/view/1641. 



Irene Stoukou, Yiannis Papargyris, David Coniam | ELT Forum 12(1) (2023) 

43 

 

Hughes, J., & Akbar, S. (2010). The influence of presentation upon examination marks. 11th 

Annual Conference of the Subject Centre for Information and Computer Sciences, 178–182. 

King, F.J., F. Rohani, C. Sanfilippo, N. White. (2008). Effects of handwritten versus computer-

written modes of communication on the quality of student essays. Center for Advancement of 

Learning and Assessment (CALA Report). http://www.cala.fsu.edu/files/writing_modes.pdf, 

2008. 

Klein, J., & Taub, D. (2005). The effect of variations in handwriting and print on evaluation of 

student essays. Assessing Writing, 10, 134–148. https://doi.org/10.1016/ j.asw.2005.05.002. 

Lim, C. P., & Wang, L. (Eds.). (2016). Blended learning for quality higher education: Selected case 

studies on implementation from Asia-Pacific. Bangkok: UNESCO Bangkok Office. 

MacCann, R., Eastment, B., & Pickering, S. (2002). Responding to free response examination 

questions: Computer versus pen and paper. British Journal of Educational Technology, 33(2), 173-

188. 

Marshall, J. C., & Powers, J. C. (1969). Writing neatness, composition errors, and essay grades. 

Journal of Educational Measurement, 6, 97–101. https://doi.org/10. 1111/j.1745-

3984.1969.tb00665.x. 

Mazzeo, J., & Harvey, A. L. (1988). The equivalence of scores from automated and conventional 

educational and psychological tests: A review of the literature. New York: College Entrance 

Examination Board. 

Mogey, N., & Fluck, A. (2015). Factors influencing student preference when comparing handwriting 

and typing for essay style examinations. British Journal of Educational Technology, 46(4), 793-802. 

Mogey, N., Paterson, J., Burk, J., & Purcell, M. (2010). Typing compared with handwriting for 

essay examinations at university: Letting the students choose. ALT-J Research in Learning 

Technology, 18, 29–47. https://doi.org/10.1080/09687761003657580. 

 

http://refhub.elsevier.com/S1075-2935(20)30031-3/sbref0060
http://refhub.elsevier.com/S1075-2935(20)30031-3/sbref0060
http://refhub.elsevier.com/S1075-2935(20)30031-3/sbref0060
http://refhub.elsevier.com/S1075-2935(20)30031-3/sbref0060
http://refhub.elsevier.com/S1075-2935(20)30031-3/sbref0060
https://doi.org/10.1016/j.asw.2005.05.002
https://doi.org/10.1111/j.1745-3984.1969.tb00665.x
http://refhub.elsevier.com/S1075-2935(18)30037-0/sbref0130
https://doi.org/10.1080/09687761003657580

