Fluency Summer Intern Project - Revision history

Schultz at 04:32, 4 August 2007

2007-08-04T04:32:02Z

← Older revision		Revision as of 04:32, 4 August 2007
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	%pos: det\|the [] n\|soccer v\|be&3S [] adv:int\|very adj\|famous .		%pos: det\|the [] n\|soccer v\|be&3S [] adv:int\|very adj\|famous .

	%err: $DET:ART:SUB:ZERO		%err: $DET:ART:SUB:ZERO; $DET:ART:LOS:A

	'''163:''' game all [/] all over the world .		'''163:''' game all [/] all over the world .

Ndjong: /* Results */

2007-07-31T17:44:26Z

Results

← Older revision		Revision as of 17:44, 31 July 2007
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	== Results ==		== Results ==
	''Lexical overlap''. To ~~check~~ whether students ~~were trying~~ to express the same meaning in the retellings, we calculated the lexical overlap between the ~~three recordings~~ in each session (the number of words that were used in both recordings, divided by the total number of words that were used in the two recordings). ~~Lexical overlap~~ was indeed higher for students who repeated their speech than for students who talked about three different topics (.15-.31 for no repetition vs. .35-.59 for repetition).		''Lexical overlap''. To verify whether students tried to express the same meaning in the retellings, we calculated the lexical overlap between the first and second recording, and between the second and third recording in each fluency training session. Lexical overlap is the number of words that were used in both recordings, divided by the total number of words that were used in the two recordings. It was indeed higher for students who repeated their speech than for students who talked about three different topics (.15-.31 for no repetition vs. .35-.59 for repetition).

	''Fluency''. Fluency developed across the three recordings in that fluent runs became longer, ~~and~~ pauses length and phonation/time ratio were stable or improved. In the first 4/3/2 training session, all students whose fluency improved in the ~~pre- and~~ posttest also showed fluency development in training. On the other hand, two out of three students whose fluency did not develop on the pre- and posttests also did not develop fluency during training. In the second training session, no fluency development took place for any students~~. In~~ the third session, fluency developed for all six students.		''Fluency''. Fluency developed across the three recordings in that fluent runs became longer, while pauses length and phonation/time ratio were stable or improved. In the first 4/3/2 training session, all students whose fluency improved from the the pretest to the posttest also showed fluency development in training. On the other hand, two out of three students whose fluency did not develop on the pre- and posttests also did not develop fluency during training. In the second training session, no fluency development took place for any students, while in the third session, fluency developed for all six students.
	Fluency development was also tracked by the number of repetitions per minute (e.g., it's ... it's imp-... important has two repetitions). This number decreased across retellings in most cases, which indicates that students had less difficulty retrieving words and structures while they were speaking.		Fluency development was also tracked by the number of repetitions per minute (e.g., ''it's ... it's imp-... important'' has two repetitions). This number decreased across retellings in most cases, which indicates that students had less difficulty retrieving words and structures while they were speaking.

	''Accuracy and complexity''. Accuracy was measured by the target-like use of subject-verb agreement (third person -s). This measure remained mostly stable, but was fairly high, over .75 for most recordings, which indicates there may have been a ceiling effect. In addition, the number of self-corrections was low, on average between 0.4 and 1.1. per minute, and remained stable across retellings, which indicates that students did not monitor their speech extensively.		''Accuracy and complexity''. Accuracy was measured by the target-like use of subject-verb agreement (third person -s). This measure remained mostly stable, but was fairly high, over .75 for most recordings, which indicates there may have been a ceiling effect. In addition, the number of self-corrections was low, on average between 0.4 and 1.1. per minute, and remained stable across retellings, which indicates that students did not monitor their speech extensively.
Line 83:		Line 83:
	''Lexical variety''. As a measure for lexical variety we used the Mean Segmental Type-Token Ratio, the mean type-token ratio for all segments of 40 words in a recording. Lexical variety did not increase in the retellings, but remained mostly stable. It did, however, increase from pretest to immediate posttest for those students whose fluency improved as well.		''Lexical variety''. As a measure for lexical variety we used the Mean Segmental Type-Token Ratio, the mean type-token ratio for all segments of 40 words in a recording. Lexical variety did not increase in the retellings, but remained mostly stable. It did, however, increase from pretest to immediate posttest for those students whose fluency improved as well.

	In summary, it seems that fluency increased during the retellings in the 4/3/2 procedure, mostly in terms of temporal measures. Accuracy for subject-verb agreement was high, and lexical variety was mostly stable. Further analyses of other, more problematic morphosyntactic structures may still reveal an increase in accuracy. ~~It seems that most~~ students ~~were able~~ to improve the fluency of their speeches during training, but only some of them were able to transfer this gain to the posttest. Future studies will need to address why this would be the case. ~~Also~~, analyses of data from more students may reveal whether this is a general pattern or occurred only for few students.		In summary, it seems that fluency increased during the retellings in the 4/3/2 procedure, mostly in terms of temporal measures. Accuracy for subject-verb agreement was high, and lexical variety was mostly stable. Further analyses of other, more problematic morphosyntactic structures may still reveal an increase in accuracy. Most students seemed to improve the fluency of their speeches during training, but only some of them were able to transfer this gain to the posttest. Future studies will need to address why this would be the case (e.g., motivation, focus on accuracy or complexity instead of fluency). In addition, analyses of data from more students may reveal whether this is a general pattern or occurred only for few students.

	== More information ==		== More information ==
	For more information about the fluency project, see [[Fostering fluency in second language learning]].		For more information about the fluency project, see [[Fostering fluency in second language learning]].

Ndjong: /* Data preparation */

2007-07-31T17:33:38Z

Data preparation

← Older revision		Revision as of 17:33, 31 July 2007
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	The %mor tier codes morphemic segments by type and part of speech. If a word could		The %mor tier codes morphemic segments by type and part of speech. If a word could
	possibly function as more than one part of speech, options are separated by a ^ symbol.		possibly function as more than one part of speech, options are separated by a ^ symbol.

			The %pos tier codes morphemic segments by type and part of speech as well. However,
			all options have been eliminated besides the correct type and part of speech for each word.

	~~The %pos tier codes morphemic segments by type and part of speech as well. However,~~
	~~all options have been eliminated besides the correct type and part of speech for each word.~~

	; CLAN: CLAN performs automatic analyses of the transcriptions. CLAN processed the CHAT files and produced the probable part of speech for each word in the transcript. Where there was still uncertainty concerning the part of speech, options were given and a choice was made by the coder. An error coding file developed by the principal investigator, Nel de Jong, was referenced for lines of the transcript containing error. From this file, CLAN produced a hierarchy of the part of speech codes in order to determine which form of the part of speech should have been used versus the form used in error by the particpant. CLAN can also perform other processes aside from morphosyntactic analysis. Such processes include the generation of a word frequency list and a lexicon of all words used in the transcript.		; CLAN: CLAN performs automatic analyses of the transcriptions. CLAN processed the CHAT files and produced the probable part of speech for each word in the transcript. Where there was still uncertainty concerning the part of speech, options were given and a choice was made by the coder. An error coding file developed by the principal investigator, Nel de Jong, was referenced for lines of the transcript containing error. From this file, CLAN produced a hierarchy of the part of speech codes in order to determine which form of the part of speech should have been used versus the form used in error by the particpant. CLAN can also perform other processes aside from morphosyntactic analysis. Such processes include the generation of a word frequency list and a lexicon of all words used in the transcript.

Ndjong at 14:14, 27 July 2007

2007-07-27T14:14:26Z

← Older revision		Revision as of 14:14, 27 July 2007
Line 73:		Line 73:

	== Results ==		== Results ==
	'''Lexical overlap'''		''Lexical overlap''. To check whether students were trying to express the same meaning in the retellings, we calculated the lexical overlap between the three recordings in each session (the number of words that were used in both recordings, divided by the total number of words that were used in the two recordings). Lexical overlap was indeed higher for students who repeated their speech than for students who talked about three different topics (.15-.31 for no repetition vs. .35-.59 for repetition).
	To check whether students were trying to express the same meaning in the retellings, we calculated the lexical overlap between the three recordings in each session (the number of words that were used in both recordings, divided by the total number of words that were used in the two recordings). Lexical overlap was indeed higher for students who repeated their speech than for students who talked about three different topics (.15-.31 for no repetition vs. .35-.59 for repetition).

	'''Fluency'''		''Fluency''. Fluency developed across the three recordings in that fluent runs became longer, and pauses length and phonation/time ratio were stable or improved. In the first 4/3/2 training session, all students whose fluency improved in the pre- and posttest also showed fluency development in training. On the other hand, two out of three students whose fluency did not develop on the pre- and posttests also did not develop fluency during training. In the second training session, no fluency development took place for any students. In the third session, fluency developed for all six students.
	Fluency developed across the three recordings in that fluent runs became longer, and pauses length and phonation/time ratio were stable or improved. In the first 4/3/2 training session, all students whose fluency improved in the pre- and posttest also showed fluency development in training. On the other hand, two out of three students whose fluency did not develop on the pre- and posttests also did not develop fluency during training. In the second training session, no fluency development took place for any students. In the third session, fluency developed for all six students.
	Fluency development was also tracked by the number of repetitions per minute (e.g., it's ... it's imp-... important has two repetitions). This number decreased across retellings in most cases, which indicates that students had less difficulty retrieving words and structures while they were speaking.		Fluency development was also tracked by the number of repetitions per minute (e.g., it's ... it's imp-... important has two repetitions). This number decreased across retellings in most cases, which indicates that students had less difficulty retrieving words and structures while they were speaking.

	'''Accuracy and complexity'''		''Accuracy and complexity''. Accuracy was measured by the target-like use of subject-verb agreement (third person -s). This measure remained mostly stable, but was fairly high, over .75 for most recordings, which indicates there may have been a ceiling effect. In addition, the number of self-corrections was low, on average between 0.4 and 1.1. per minute, and remained stable across retellings, which indicates that students did not monitor their speech extensively.
	Accuracy was measured by the target-like use of subject-verb agreement (third person -s). This measure remained mostly stable, but was fairly high, over .75 for most recordings, which indicates there may have been a ceiling effect. In addition, the number of self-corrections was low, on average between 0.4 and 1.1. per minute, and remained stable across retellings, which indicates that students did not monitor their speech extensively.

	'''Lexical variety'''		''Lexical variety''. As a measure for lexical variety we used the Mean Segmental Type-Token Ratio, the mean type-token ratio for all segments of 40 words in a recording. Lexical variety did not increase in the retellings, but remained mostly stable. It did, however, increase from pretest to immediate posttest for those students whose fluency improved as well.
	As a measure for lexical variety we used the Mean Segmental Type-Token Ratio, the mean type-token ratio for all segments of 40 words in a recording. Lexical variety did not increase in the retellings, but remained mostly stable. It did, however, increase from pretest to immediate posttest for those students whose fluency improved as well.

	In summary, it seems that fluency increased during the retellings in the 4/3/2 procedure, mostly in terms of temporal measures. Accuracy for subject-verb agreement was high, and lexical variety was mostly stable. Further analyses of other, more problematic morphosyntactic structures may still reveal an increase in accuracy. It seems that most students were able to improve the fluency of their speeches during training, but only some of them were able to transfer this gain to the posttest. Future studies will need to address why this would be the case. Also, analyses of data from more students may reveal whether this is a general pattern or occurred only for few students.		In summary, it seems that fluency increased during the retellings in the 4/3/2 procedure, mostly in terms of temporal measures. Accuracy for subject-verb agreement was high, and lexical variety was mostly stable. Further analyses of other, more problematic morphosyntactic structures may still reveal an increase in accuracy. It seems that most students were able to improve the fluency of their speeches during training, but only some of them were able to transfer this gain to the posttest. Future studies will need to address why this would be the case. Also, analyses of data from more students may reveal whether this is a general pattern or occurred only for few students.

Ndjong: Added results

2007-07-27T14:13:17Z

Added results

← Older revision		Revision as of 14:13, 27 July 2007
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	== Purpose of the Project ==		== Purpose of the Project ==

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	Data was collected under two different conditions. The first condition was that students would repeat their speech, developed from a prompt concerning a singular topic, on three occasions. The second condition was that students would be asked to address three different topics. Data consists of recordings from the 4/3/2 task, as well as from pre- and posttests. Articulation rate, morphology, syntax, mean length of fluent runs, and mean length of pauses will all be examined.		Data was collected under two different conditions. The first condition was that students would repeat their speech, developed from a prompt concerning a singular topic, on three occasions. The second condition was that students would be asked to address three different topics. Data consists of recordings from the 4/3/2 task, as well as from pre- and posttests. Articulation rate, morphology, syntax, mean length of fluent runs, and mean length of pauses will all be examined.

	== Tools Used to Analyze Data ==		== Data preparation ==

			Recordings from the pre- and posttests were transcribed and analyzed for fluency before. In the present project, recordings from the training itself were transcribed and coded for morphosyntactic accuracy, parts of speech, and repetitions and reformulations.


			''Tools Used to Analyze Data''

	; PRAAT: PRAAT is a computer program that allows multiple tier transcriptions to be displayed along with sound wave files. For the purposes of this project, PRAAT was used to transcribe the 4/3/2 recordings. A spectrogram was referenced while listening to the sound files in order to determine appropriate time intervals of pauses and speech utterances.		; PRAAT: PRAAT is a computer program that allows multiple tier transcriptions to be displayed along with sound wave files. For the purposes of this project, PRAAT was used to transcribe the 4/3/2 recordings. A spectrogram was referenced while listening to the sound files in order to determine appropriate time intervals of pauses and speech utterances.
Line 24:		Line 28:


	*'''Example of CHAT format'''		''Example of CHAT format''

	'''163:''' the [] soccer is [] very famous: .		'''163:''' the [] soccer is [] very famous: .
Line 50:		Line 54:
	; CLAN: CLAN performs automatic analyses of the transcriptions. CLAN processed the CHAT files and produced the probable part of speech for each word in the transcript. Where there was still uncertainty concerning the part of speech, options were given and a choice was made by the coder. An error coding file developed by the principal investigator, Nel de Jong, was referenced for lines of the transcript containing error. From this file, CLAN produced a hierarchy of the part of speech codes in order to determine which form of the part of speech should have been used versus the form used in error by the particpant. CLAN can also perform other processes aside from morphosyntactic analysis. Such processes include the generation of a word frequency list and a lexicon of all words used in the transcript.		; CLAN: CLAN performs automatic analyses of the transcriptions. CLAN processed the CHAT files and produced the probable part of speech for each word in the transcript. Where there was still uncertainty concerning the part of speech, options were given and a choice was made by the coder. An error coding file developed by the principal investigator, Nel de Jong, was referenced for lines of the transcript containing error. From this file, CLAN produced a hierarchy of the part of speech codes in order to determine which form of the part of speech should have been used versus the form used in error by the particpant. CLAN can also perform other processes aside from morphosyntactic analysis. Such processes include the generation of a word frequency list and a lexicon of all words used in the transcript.

	~~== Error Coding==~~

	'''Errors which were coded include:~~'''~~		''Error coding''

			Errors which were coded include:

	* Morphological errors such as:		* Morphological errors such as:
Line 67:		Line 72:
	* Syntactical errors will eventually be addressed, but not at this moment.		* Syntactical errors will eventually be addressed, but not at this moment.

	== ~~For~~ more information ==		== Results ==
	[[Fostering fluency in second language learning]]		'''Lexical overlap'''
			To check whether students were trying to express the same meaning in the retellings, we calculated the lexical overlap between the three recordings in each session (the number of words that were used in both recordings, divided by the total number of words that were used in the two recordings). Lexical overlap was indeed higher for students who repeated their speech than for students who talked about three different topics (.15-.31 for no repetition vs. .35-.59 for repetition).

			'''Fluency'''
			Fluency developed across the three recordings in that fluent runs became longer, and pauses length and phonation/time ratio were stable or improved. In the first 4/3/2 training session, all students whose fluency improved in the pre- and posttest also showed fluency development in training. On the other hand, two out of three students whose fluency did not develop on the pre- and posttests also did not develop fluency during training. In the second training session, no fluency development took place for any students. In the third session, fluency developed for all six students.
			Fluency development was also tracked by the number of repetitions per minute (e.g., it's ... it's imp-... important has two repetitions). This number decreased across retellings in most cases, which indicates that students had less difficulty retrieving words and structures while they were speaking.

			'''Accuracy and complexity'''
			Accuracy was measured by the target-like use of subject-verb agreement (third person -s). This measure remained mostly stable, but was fairly high, over .75 for most recordings, which indicates there may have been a ceiling effect. In addition, the number of self-corrections was low, on average between 0.4 and 1.1. per minute, and remained stable across retellings, which indicates that students did not monitor their speech extensively.

			'''Lexical variety'''
			As a measure for lexical variety we used the Mean Segmental Type-Token Ratio, the mean type-token ratio for all segments of 40 words in a recording. Lexical variety did not increase in the retellings, but remained mostly stable. It did, however, increase from pretest to immediate posttest for those students whose fluency improved as well.

			In summary, it seems that fluency increased during the retellings in the 4/3/2 procedure, mostly in terms of temporal measures. Accuracy for subject-verb agreement was high, and lexical variety was mostly stable. Further analyses of other, more problematic morphosyntactic structures may still reveal an increase in accuracy. It seems that most students were able to improve the fluency of their speeches during training, but only some of them were able to transfer this gain to the posttest. Future studies will need to address why this would be the case. Also, analyses of data from more students may reveal whether this is a general pattern or occurred only for few students.

			== More information ==
			For more information about the fluency project, see [[Fostering fluency in second language learning]].

Schultz: /* Tools Used to Analyze Data */

2007-07-17T14:43:49Z

Tools Used to Analyze Data

← Older revision		Revision as of 14:43, 17 July 2007
Line 42:		Line 42:


	The %mor tier codes morphemic segments by type and part of speech. If a word could possibly function as more than one part of speech, options are separated by a ^ symbol.		The %mor tier codes morphemic segments by type and part of speech. If a word could
			possibly function as more than one part of speech, options are separated by a ^ symbol.

	The %pos tier codes morphemic segments by type and part of speech as well. However, all options have been eliminated besides the correct type and part of speech for each word.		The %pos tier codes morphemic segments by type and part of speech as well. However,
			all options have been eliminated besides the correct type and part of speech for each word.

	; CLAN: CLAN performs automatic analyses of the transcriptions. CLAN processed the CHAT files and produced the probable part of speech for each word in the transcript. Where there was still uncertainty concerning the part of speech, options were given and a choice was made by the coder. An error coding file developed by the principal investigator, Nel de Jong, was referenced for lines of the transcript containing error. From this file, CLAN produced a hierarchy of the part of speech codes in order to determine which form of the part of speech should have been used versus the form used in error by the particpant. CLAN can also perform other processes aside from morphosyntactic analysis. Such processes include the generation of a word frequency list and a lexicon of all words used in the transcript.		; CLAN: CLAN performs automatic analyses of the transcriptions. CLAN processed the CHAT files and produced the probable part of speech for each word in the transcript. Where there was still uncertainty concerning the part of speech, options were given and a choice was made by the coder. An error coding file developed by the principal investigator, Nel de Jong, was referenced for lines of the transcript containing error. From this file, CLAN produced a hierarchy of the part of speech codes in order to determine which form of the part of speech should have been used versus the form used in error by the particpant. CLAN can also perform other processes aside from morphosyntactic analysis. Such processes include the generation of a word frequency list and a lexicon of all words used in the transcript.

Schultz: /* Tools Used to Analyze Data */

2007-07-17T14:37:45Z

Tools Used to Analyze Data

← Older revision		Revision as of 14:37, 17 July 2007
Line 41:		Line 41:
	%pos: n\|game qn\|all adv:loc\|over det\|the n\|world .		%pos: n\|game qn\|all adv:loc\|over det\|the n\|world .


			The %mor tier codes morphemic segments by type and part of speech. If a word could possibly function as more than one part of speech, options are separated by a ^ symbol.

			The %pos tier codes morphemic segments by type and part of speech as well. However, all options have been eliminated besides the correct type and part of speech for each word.

	; CLAN: CLAN performs automatic analyses of the transcriptions. CLAN processed the CHAT files and produced the probable part of speech for each word in the transcript. Where there was still uncertainty concerning the part of speech, options were given and a choice was made by the coder. An error coding file developed by the principal investigator, Nel de Jong, was referenced for lines of the transcript containing error. From this file, CLAN produced a hierarchy of the part of speech codes in order to determine which form of the part of speech should have been used versus the form used in error by the particpant. CLAN can also perform other processes aside from morphosyntactic analysis. Such processes include the generation of a word frequency list and a lexicon of all words used in the transcript.		; CLAN: CLAN performs automatic analyses of the transcriptions. CLAN processed the CHAT files and produced the probable part of speech for each word in the transcript. Where there was still uncertainty concerning the part of speech, options were given and a choice was made by the coder. An error coding file developed by the principal investigator, Nel de Jong, was referenced for lines of the transcript containing error. From this file, CLAN produced a hierarchy of the part of speech codes in order to determine which form of the part of speech should have been used versus the form used in error by the particpant. CLAN can also perform other processes aside from morphosyntactic analysis. Such processes include the generation of a word frequency list and a lexicon of all words used in the transcript.

Schultz at 14:19, 9 July 2007

2007-07-09T14:19:26Z

← Older revision		Revision as of 14:19, 9 July 2007
Line 1:		Line 1:

	== Purpose of the Project ==		== Purpose of the Project ==

Schultz: /* Error Coding */

2007-07-09T14:19:01Z

Error Coding

← Older revision		Revision as of 14:19, 9 July 2007
Line 55:		Line 55:
	** relative pronoun (loss, addition, substitution)		** relative pronoun (loss, addition, substitution)
	** conjunction (loss, addition, substitution)		** conjunction (loss, addition, substitution)

			*Repetitions were marked by: [/] which represents an exact repetition, [//] which represents retracing with correction, and [///] which represents retracing with reformulation.

	* Syntactical errors will eventually be addressed, but not at this moment.		* Syntactical errors will eventually be addressed, but not at this moment.

Schultz at 14:14, 9 July 2007

2007-07-09T14:14:09Z

@@ Line 43: / Line 43: @@
 ; CLAN: CLAN performs automatic analyses of the transcriptions.  CLAN processed the CHAT files and produced the probable part of speech for each word in the transcript.  Where there was still uncertainty concerning the part of speech, options were given and a choice was made by the coder.  An error coding file developed by the principal investigator, Nel de Jong, was referenced for lines of the transcript containing error.  From this file, CLAN produced a hierarchy of the part of speech codes in order to determine which form of the part of speech should have been used versus the form used in error by the particpant.  CLAN can also perform other processes aside from morphosyntactic analysis.  Such processes include the generation of a word frequency list and a lexicon of all words used in the transcript.
 == For more information ==
 [[Fostering fluency in second language learning]]