LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Fowler, Alison M.L. (2006)
Publisher: Universiteit Antwerpen
Languages: English
Types: Unknown
Subjects: QA76
An important advantage of online assessment work is that answer data can be easily stored and later analysed with a view to establishing the efficacy of the assessment methodology. A 5-year study of the effectiveness of online grammar exercises has been carried out at the University of Kent. The exercises featured require input in the form of whole sentences (since this is a more authentic test of language skills than single word input or multiple choice). Error feedback is generic (indicating where errors have occurred) rather than specific (indicating the exact nature of the errors) because the error-diagnosis system has been designed to be completely language independent. The study aimed to gauge whether this type of feedback is effective - in terms of enabling students to: · identify the types of mistakes in their input; · rectify the mistakes; · learn from the mistakes and apply that learning to subsequent problems There was initial concern that the generic feedback might not provide enough detail to enable users to understand and correct their errors - however extensive use by the University's Spanish department has shown that this type of mark-up is very effective. Chapelle (1998) stresses that it is important for learners to be given the opportunity to correct their linguistic errors. Users of this system, having failed to answer a question correctly on their first attempt, are permitted a second attempt. It is abundantly clear from the logged data that where users make mistakes in their first attempt (and they generally do since the material is designed to be testing), there is almost always a significant improvement in attempt two. This alone would be enough to show that the feedback mode is effective. However this was not enough to prove the pedagogical efficacy of this means of exercise presentation. Therefore more detailed analysis was performed. Over several years of trials more than 100,000 answers have been logged and every answer has been analysed. It can be shown that, for well-designed exercises, as students progress through an exercise they improve in three ways: · more questions are answered correctly on the first attempt; · overall questions scores (i.e. the average of 1st and 2nd attempts at questions) improve; · thinking time for formulating answers decreases The degree of increase in accuracy and decrease in thinking time is exercise-dependent, but the overall picture shows clearly that the generic, language-independent feedback is indeed effective. Moreover, it is easy to identify poorly designed exercises since they do not exhibit the characteristics listed above.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Ba ng s, P. (2003). Eng ag ing the le a rne r - ho w to a utho r fo r b e st fe e db a c k. In U. Fe lix (Ed.), La ng uag e le a rning o nline - To wards b e st pra c tic e (pp. 81-96). Lisse : Swe ts & Ze itling e r.
    • Cha pe lle , C. (1998). Multime dia CALL: Le sso ns to be le a rne d fro m re se a rc h o n instruc te d SLA. Lang uag e Le arning and Te c hno lo g y, 2(1), 22-34. Available : http:/ / llt.msu.e du/ vo l2num1/ .
    • Cha pe lle , C. (2001). Co mpute r a pplic a tio ns in se c o nd lang ua g e a c quisitio n: fo unda tio ns fo r te ac hing , te sting and re se arc h. Camb ridg e : Ca mb ridg e Unive rsity Pre ss.
    • Do dig o vic , M. (2005). Artific ial inte llig e nc e in se c o nd lang uag e le arning : raising e rro r a wa re ne ss. Cle ve do n: Multiling ual Ma tte rs Ltd.
    • De lmo nte , R. (2003). Ling uistic kno wle dg e a nd re a so ning fo r dia g no sis a nd fe e dba c k. CALICO Jo urnal, 20(3), 513-532.
    • Fulc he r, G. (2000). Co mpute rs in lang ua g e te sting . In P. Bre tt, & G. Mo tte ram (Eds.), A spe c ial inte re st in c o mpute rs - Le a rning and te a c hing with info rma tio n a nd c o mmunic a tio ns te c hno lo g ie s (pp. 93-107). Whitstable : IATEFL.
    • Ja me s, C. (1999). La ng uag e Awa re ne ss: Implic a tio ns fo r the La ng uag e Curric ulum. Lang uag e , Culture and Curric ulum, 12(1), 96-116.
    • He ift, T. (2004). Co rre c tive fe e dbac k and le arne ruptake in CALL. Re CALL, 16(2), 416-431.
    • Hé mard, D. (2003). Lang ua g e le arning o nline : de sig ning to wa rds use r a c c e pta b ility. In U. Fe lix (Ed.), La ng uag e le arning o nline - To wards be st prac tic e (pp. 21-42). Lisse : Swe ts & Ze itling e r.
    • L' Haire , S., & Vande ve nte r Faltin, A. (2003). Erro r dia g no sis in the Fre e Te xt pro je c t. CALICO Jo urnal, 20(3), 481-495.
    • Lo ng , M.H. (1991). Fo c us o n fo rm: a de sig n fe a ture in la ng ua g e te a c hing me tho do lo g y. In K. de Bo t, R. Ginsb e rg , & C. Kra msc h (Eds.), Fo re ig n lang ua g e re se a rc h in c ro ss-c ultura l pe rspe c tive (pp. 39-52). Amste rda m: Jo hn Be nja mins.
    • Me nze l, W., & Sc hrö de r, I. (1999). Erro r dia g no sis fo r la ng ua g e le a rning syste ms. La ng ua g e Pro c e ssing in CALL, Re CALL(spe c ial e ditio n, May 1999), 20-30.
    • Ro b inso n, P. (1995). Atte ntio n, me mo ry a nd the ' no tic ing ' hypo the sis. Lang uag e Le arning , 45(2), 283-331.
    • Sc hwind, C.B. (1990). An inte llig e nt la ng ua g e tuto ring syste m. Inte rnatio nal Jo urnal o f ManMac hine Studie s, 33, 557-579
    • Sc hulze , M. (1999). Fro m the de ve lo pe r to the le a rne r: de sc ribing g ra mma r - le a rning g ra mma r. Re CALL, 11(1), 117-124.
    • Tsc hic ho ld, C. (2003). Le xic ally drive n e rro r de te c tio n a nd c o rre c tio n. CALICO Jo urnal, 20(3), 549-559.
    • Va n de r Linde n, E. (1993). Do e s fe e db a c k e nha nc e c o mpute r-a ssiste d la ng ua g e le a rning . Co mpute rs & Educ atio n, 21(1-2), 61-65.
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

Share - Bookmark

Download from

Cite this article