1. Syntactic variables in pupils' writings: a comparison of hand-written and PC-written texts Bård Uri Jensen University of Bergen / Hedmark University College

2. Contents  Purpose  Background theory  Presentation of text corpus  Research questions  Results  Discussion

3. Purpose / Aim  Pupils’ writing in school by hand or on PC  Does production mode affect syntax ?  Syntactic variables  Lexical variables

4. Background theory / previous research  Word processing Russell 1999 Harrington, Shermis & Rollins 2000 Kellogg & Mueller 1993  Computer-mediated communication Baron 1998 Crystal 2001 Hård av Segerstad 2002  Production speed Horowitz & Berkowitz 1964  Written and spoken language  differences resulting from production speed Allwood 1998 Biber 1988 Halliday 1989

5. Research questions  How are the following variables affected by production mode in pupils’ writing?  Lexical density  Lexical diversity  Rate of subordination Biber 1988, Halliday 1989  Rate of modal particles  Rate of certain kinds of topic markers Faarlund, Lie & Vannebo 1997

6. Research questions  How are the following variables affected by production mode in pupils’ writing?  Lexical density  Rate of subordination Biber 1988, Halliday 1989  Rate of modal particles Faarlund, Lie & Vannebo 1997

7. Text collection  20 pupils in 11th year (16 years old)  Three hours writing session  little opportunity for revision / rewriting  No Internet connection Text A (Day 1) Text B (Day 2) Pupil1-10HandPC Pupil11-20PCHand  Text length

8. Subordination (independent clauses)  Subjunction count  At, om, som, fordi, når, så, hvis, hvordan, …  That, whether, which/that, because, when, so that, if, how, …  Å (+ infinitive)  To (+ infinitive)  Traces of som and at. 1)Han sa han skulle komme. He said he would come. 2)Bilen jeg kjører, er en Toyota. The car I drive is a Toyota.  (Question-type 3)Hadde jeg ikke kommet, ville det ikke ha skjedd. Had I not come, it would not have happened. )

9. Results: Subordination  Significant differences in subordinations by number of (graphic) sentences. One-way ANOVA s<.05HandPC Mean1.151.45

10. Modal particles  Jo, vel, nok, da, nå, visst  Jo = Known to both sender and receiver. 1)Jeg kjører jo Toyota. I drive a Toyota, you know.  Vel = Uncertainty and appeals to receiver’s knowledge. 2)Jenter leser vel mer bøker. Girls read books more, don’t they?  Nok = Expresses probability. 3)Gutter driver nok mer med data. I think boys use their computer more. Boys probably use their computer more.

11. Modal particles and text type  Frequency per 1000 words  No significant differences related to production mode  Jo, nok are slightly more frequent in PC-texts  Vel is slightly less frequent in PC-text  Significant mean differences as function of question One-way ANOVA, s<.05 Text A Text B Mean / 1000 8.33.0

12. Modal particles and text length  Significant positive correlation:  Difference in rate of modal particles with production mode  Total text length produced by pupil  Pearson’s correlation 0.57, s<.01  Pupils who generally write long texts use more modal particles in PC-texts  Pupils who write long texts:  have good writing skills?  are motivated?  utilise speed to produce ”fluently”?  get carried away?

13. Results: Lexical density  Ratio of lexical words to total words  Nouns, adjectives and verbs  Minus function verbs å ha (to have), å være (to be)  Lexical adverbs not included  Production mode alone shows no influence  Significant negative correlation between  Difference in lexical density between production modes  Difference in text length between production modes  Pearson’s correlation -.61, s<.01  No correlation with total text length!

14. Discussion  Problem of grammatical unit Problem of grammatical unit Problem of grammatical unit  Differentiating between different categories of pupils Differentiating between different categories of pupils Differentiating between different categories of pupils  text length  text length difference  Corpus size Corpus size Corpus size  Pupils’ knowledge of norms? Pupils’ knowledge of norms? Pupils’ knowledge of norms?

15. References  Allwood, Jens (1998). Some Frequency based Differences between Spoken and Written Swedish. In proceedings from the XVI:th Scandinavian Conference of Linguistics,  Department of Linguistics, University of Turku Baron, N. S. (1998). Letters by phone or speech by other means: the linguistics of email. Language & Communication, 18(2), 133-170.  Biber, D. (1988). Variation across speech and writing. New York: Cambridge University Press.  Crystal, D. (2001). Language and the Internet. Cambridge: Cambridge University Press.  Faarlund, J. T., Lie, S., og Vannebo, K. I. (1997). Norsk referansegrammatikk. Oslo: Universitetsforlaget.  Halliday, M. A. K. (1989). Spoken and written language (2nd ed.). Oxford: Oxford University Press.  Harrington, S., Shermis, M. D., og Rollins, A. L. (2000). The influence of word processing on English placement test results. Computers and Composition, 17(2), 197-210.  Horowitz, M. W., og Berkowitz, A. (1964). Structural advantage of the mechanism of spoken expression as a factor in differences in spoken and written expression. Perceptual and motor skills, 19, 619-625.  Hård af Segerstad, Y. (2002). Use and Adaptation of Written Language to the Conditions of Computer-mediated Communication. Göteborg University, Göteborg.  Kellogg, R. T., og Mueller, S. (1993). Performance amplification and process restructuring in computer-based writing. International Journal of Man-Machine Studies, 39(1), 33-49.  Russell, M. (1999). Testing on computers: A follow-up study comparing performance on computer and on paper. Education Policy Analysis Archives, 7(20).

16. Corpus size  Difficult to obtain significance  Some substantial differences / correlations  Less substantial differences may be significant in a larger corpus.

17. Unit of measurement  Basic principle:  Number of occurances per possible places of use  Subordination  Per graphic sentence (i.e. between )  Should be per independent clause.  Requires time-consuming manual analysis.  Modal particles  Per 1000 words  Should be per indpendent clause  Lexical density  Per total number of words 

18. Knowledge of norms  Long sentences,  Independent clauses often piled onto each other  Without conjunctions  Without full stops  Without commas, sometimes  Often seem quite oral in nature  If pupils don’t know the norms, can’t be expected to strive towards them  Maybe differences will only show in pupils with good writing skills?

19. Categorization of pupils

20. Results: Lexical diversity Distribution of word frequency  Written 10 words = 19% 10 words = 19% 50 words = 38% 50 words = 38% 10.000 words = 87%  Hand 10 words = 24% 10 words = 24% 50 words = 53% 50 words = 53% 700 words = 91%  Spoken 10 words = 23% 50 words = 52% 10.000 words = 97% Allwood 1998  PC 10 words = 24% 50 words = 53% 700 words = 90%

21. Hand PC 1detit4,004,0detit4,014,0 2eris3,817,8eris3,627,6 3ogand3,1711,0ogand3,3611,0 4som that (adj) 2,3013,3som 2,2913,3 5ikkenot2,1915,5å to (inf.) 2,2415,5 6iin1,9117,4påon1,8917,4 7påon1,8419,2at that (subs) 1,8319,2 8at 1,7121,0ikkenot1,7621,0 9å to (inf.) 1,7022,7dethey1,7422,7 10dethey1,6524,3forfor1,6324,4 11jegI1,5825,9ena/an1,4825,8 12medwith1,4127,3jegI1,4527,3 13ena/an1,3328,6iin1,4228,7

22. Hand PC 1detit4,004,0detit4,014,0 2eris3,817,8eris3,627,6 3ogand3,1711,0ogand3,3611,0 4som that (adj) 2,3013,3som 2,2913,3 5ikkenot2,1915,5å to (inf.) 2,2415,5 6iin1,9117,4påon1,8917,4 7påon1,8419,2at that (subs) 1,8319,2 8at 1,7121,0ikkenot1,7621,0 9å to (inf.) 1,7022,7dethey1,7422,7 10dethey1,6524,3for 1,6324,4 11jegI1,5825,9ena/an1,4825,8 12medwith1,4127,3jegI1,4527,3 13ena/an1,3328,6iin1,4228,7

23. Hand PC 1detit4,004,0detit4,014,0 2eris3,817,8eris3,627,6 3ogand3,1711,0ogand3,3611,0 4som that (adj.) 2,3013,3som 2,2913,3 5ikkenot2,1915,5å to (inf.) 2,2415,5 6iin1,9117,4påon1,8917,4 7påon1,8419,2at that (subs) 1,8319,2 8at 1,7121,0ikkenot1,7621,0 9å to (inf.) 1,7022,7dethey1,7422,7 10dethey1,6524,3forfor1,6324,4 11jegI1,5825,9ena/an1,4825,8 12medwith1,4127,3jegI1,4527,3 13ena/an1,3328,6iin1,4228,7

24. Hand PC 1detit4,004,0detit4,014,0 2eris3,817,8eris3,627,6 3ogand3,1711,0ogand3,3611,0 4som that (adj.) 2,3013,3som 2,2913,3 5ikkenot2,1915,5å to (inf.) 2,2415,5 6iin1,9117,4påon1,8917,4 7påon1,8419,2at that (subs) 1,8319,2 8at 1,7121,0ikkenot1,7621,0 9å to (inf.) 1,7022,7dethey1,7422,7 10dethey1,6524,3forfor1,6324,4 11jegI1,5825,9ena/an1,4825,8 12medwith1,4127,3jegI1,4527,3 13ena/an1,3328,6iin1,4228,7

25. Hand PC 1detit4,004,0detit4,014,0 2eris3,817,8eris3,627,6 3ogand3,1711,0ogand3,3611,0 4som that (adj.) 2,3013,3som 2,2913,3 5ikkenot2,1915,5å to (inf.) 2,2415,5 6iin1,9117,4påon1,8917,4 7påon1,8419,2at that (subs) 1,8319,2 8at 1,7121,0ikkenot1,7621,0 9å to (inf.) 1,7022,7dethey1,7422,7 10dethey1,6524,3forfor1,6324,4 11jegI1,5825,9ena/an1,4825,8 12medwith1,4127,3jegI1,4527,3 13ena/an1,3328,6iin1,4228,7

26. Density in written and spoken language Written: Investment in a rail facility implies a long-term commitment. Spoken: If you invest in a rail facility, this implies that you are going to be committed for a long term. Halliday (1989)