Theory Wiki - User contributions [en]

Feature validity

2011-08-31T13:12:43Z

Pierrehernandez:

[[Category:Glossary]]
[[Category:Coordinative Learning]]

The feature validity of a [[knowledge component]] measures how well the [[features]] associated with the mental representation of the knowledge component match the features present during all situations where the component should be recalled.

A student has acquired a knowledge component (KC) with high feature validity when the retrieval features of that knowledge component are all relevant and none are irrelevant. Through the learning process of [[refinement]] a learner may modify an existing KC to produce a new one with higher feature validity.

Feature validity is a generalization of the standard concept of cue validity. Cues are usually understood to be perceptual or at least rapidly computed (McDonald & MacWhinney, 1989). The term “features” includes cues as well as higher level properties, such as those used by experts but not novices (Chi, Feltovitch, & Glaser, 1981).

See the [[Booth]] page for examples of knowledge components with different levels of feature validity.

=== References ===
* Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.
* McDonald, J. L., & MacWhinney, B. (1989). Maximum likelihood models for sentence processing research. In B. MacWhinney & E. Bates (Eds.), The crosslinguistic study of sentence processing (pp. 397-421). New York: Cambridge University Press.
* Zhu X., Lee Y., Simon H.A., & Zhu, D. (1996). Cue recognition and cue elaboration in learning from examples. In Proceedings of the National Academy of Sciences 93, (pp. 1346±1351).
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Explicit instruction

2011-08-31T13:12:28Z

Pierrehernandez:

Explicit instruction occurs in an instructional task that provides the learner with specific information or directions about what is to be learned from the task. Explicit instruction often comes in the form of rules or verbal statements that provide guidance to the student about what is to be learned. Although explicit instruction contrasts with [[implicit instruction]], instructional tasks are often graded with elements of each. Thus instruction can be relatively explicit or relatively implicit. Explicit instruction works through [[feature focusing]], drawing the learner's attention to the valid or critical features ([[feature validity]]) of the content to be learned. Implicit learning can be designed to promote feature focusing, as well, although often it does not.

[[Instructional explanation]]s are one specific type of explicit instruction.

[[Category:Glossary]]
[[Category:Independent Variables]]
[[Category:Help Tutor]]
[[Category:PSLC General]]
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Explicit and implicit knowledge of infinitival and gerundival verb complements in L2 speech

2011-08-31T13:12:17Z

Pierrehernandez:

[This page is under construction]

== Abstract ==

The present study set out to investigate whether students were able to use their explicit knowledge of verb complements during spontaneous speech. Spontaneous speech data was elicited from 32 high-intermediate English as a Second Language (ESL) students. Results show that students made errors that were not typical of explicit knowledge, in that they did not merely switch verb complement types. Instead, they used many unmarked complements or doubly-marked complements. This suggests they relied on implicit knowledge, possible acquired from exposure to the target language. Variable input due to matrix verbs that can take either type of complement may have affected the acquisition of the correct complement forms.

Teachers cannot assume that the variable matrix verbs are the easiest ones because they allow both forms. Instead, these verbs may need additional instruction. In addition, instruction needs to include sufficient practice in order to stimulate the acquisition of implicit knowledge.

== Background and significance ==

It is a common phenomenon that second language (L2) learners know many grammatical rules but do not apply them correctly and consistently when speaking. In class, students often acquire explicit knowledge of grammatical structures, but this is slow to use and requires attentional resources (De Jong, 2005; DeKeyser, in press; Ellis, 2005, 2006; Hulstijn, 2002). Therefore, to be able to speak with high fluency and grammatical accuracy, it is necessary for students to acquire implicit knowledge, because it is faster to use and does not require as much attentional resources.

The present study set out to investigate whether students were able to use their explicit knowledge of verb complements during spontaneous speech. If explicit knowledge is used, L2 learners may choose the two verb complement forms equally; if implicit knowledge is used, L2 learners might show preference for one of the two forms. If explicit knowledge is being used, it can be expected that errors would involve switching the two types of complements. However, if other errors would occur, implicit knowledge is likely being used and possibly L1 transfer may be involved.

== Research question ==

* Do high intermediate ESL students rely on their explicit knowledge of a variable grammatical structure (verb complements) during spontaneous speech, or do they rely more on implicit knowledge instead?

== Method ==
Participants were 32 students enrolled in high intermediate Speaking classes. They had been taught the grammatical structure of English verb complements, in that some verbs take a to-infinitive, some take a gerund, and others take either a to-infinitive or gerund as a complement. The students took part in three sessions of the 4/3/2 task, in each of which they spoke three times, for four, three, and two minutes, respectively. Fifteen students repeated the same topic during a session, while 8 other students spoke about three different topics. In total, 27 minutes of speech were elicited from each student.

The speeches were transcribed in PRAAT, and annotated in CHAT/CLAN (the software of the Childes project). Codes were added for parts of speech, errors, and retracings (repetitions, corrections, and reformulations). All verb complements (correct and errors) were retrieved to be further analyzed.

== Independent variables ==
* Type of matrix verb requirement
**to-infinitive
**gerund
**either

== Dependent variables ==
* Type of complement produced
**to-infinitive
**gerund
**ambiguous
*error
**appropriate
**not appropriate

== Hypotheses ==

* If students rely more on explicit knowledge of the target structure, errors will involve mostly switching of the verb complement types.
* If students rely more on implicit knowledge of the target structure, other types of errors will be made, e.g., use of the default form.

== Findings ==

''Verb complement use and accuracy''
All participants attempted the target structure; the range was 7-44 verb complement attempts per student. The mean accuracy per student was 82.2% (12%) with a range of 60 – 100%.

Matrix verbs that require a gerund were produced least often (16) but most accurately. More instances were produced of matrix verbs that require an infinitive (152) or allow either a gerund or a to-infinitive (352). The error rate for both groups of these matrix verbs was around 15%. The error pattern for these types of matrix verbs was very similar.

Errors were rarely a result of a mismatch between matrix verb and verb complement, as when a gerund would be used instead of a to-infinitive or vice versa (e.g., I enjoy watch TV, I want studying). Instead, the most common error was producing only the root verb with neither the infinitival to marker nor the gerundival -ing, while the next common error was using both markers.

''L1 transfer''
To explore the origins of the preference for using gerunds and to-infinitive markers, we investigate L1 influence. The Korean L1 and Chinese L1 speakers had much lower means of verb complement structures per student (about 16) than the other L1 groups (over 30). The Korean L1 students and the single Russian speaker had slightly lower accuracy (about 73%) than the other L1 groups.

It might be predicted that students from L1s with less morphology produced the ambiguous verb complements with neither morpheme. This expectation was not supported because L1 did not seem to impact the type of errors.

== Explanation ==

If explicit knowledge were used, it would be likely that students knew the correct verb complement forms, but made errors selecting the appropriate complement for a particular matrix verb. Thus, gerunds would be used instead of to-infinitives, and vice versa. In addition, there would be an even distribution of the two options. Instead, the most common error was a lack of either morphological marking, only producing the root verb for the verb complement.

A possible explanation is that the online demands of the production task and trying to apply explicit knowledge about the matrix verb requirements were too great, resulting in no morphological marking. Therefore, the students may have relied on their implicit knowledge during this production task.

One type of implicit knowledge may have stemmed from experience with the target language. Of the matrix verbs that can take either verb complement form, 13% of the verb complements were ambiguously produced. This finding might reflect the difficulty of learning a structure that has varied input; there is no clear collocation with the matrix verbs, especially in the variable category (like, prefer, love) despite the high frequency of these verbs.

Teachers cannot assume that the variable matrix verbs are the easiest ones because they allow both forms. Instead, these verbs may need additional instruction. In addition, instruction needs to include sufficient practice in order to stimulate the acquisition of implicit knowledge.

== Further information ==

The results of this study were presented at the GURT conference in March 2009, and are under review for the proceedings. The study was performed by Mary Lou Vercellotti from the University of Pittsburgh and Dr. Nel de Jong from Queens College of CUNY.

This study was part of the project [[Fostering_fluency_in_second_language_learning|Fostering fluency in second language learning]] by Nel de Jong, Laura Halderman, and Charles Perfetti.
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Example-rule coordination principle

2011-08-31T13:12:03Z

Pierrehernandez:

==Brief statement of principle==
Instruction that combines or helps students' combine learning from examples and learning of or from rules tends to be more effective than instruction that includes the same examples and rules but does not help students combine them.

==Description of principle==
Example-rule coordination refers to a class of [[instructional method]]s that involve combining instructional [[example]]s with other forms of instruction including [[self-explanation]], problem-solving practice, [[analogical comparison]]. Coordination support may occur through explicit prompting for self-explanations, interleaving [[worked examples]] and problems, fading [[assistance]] from worked examples to problems.
===Operational definition===
===Examples===
Studies exploring various forms of example-rule coordination include: Butcher's [[Mapping Visual and Verbal Information: Integrated Hints in Geometry (Aleven & Butcher)|integrated hints]] in Geometry, Booth's [[Booth |corrective self-explanation]] in Algebra, McLaren's [[Stoichiometry_Study | worked example interleaving]] in Chemistry, Eskenazi's [[REAP_main |vocabulary example personalization]] in English, Ringenberg's [[Ringenberg_Examples-as-Help |example-based help]] in Physics, Anthony's [[Effect of adding simple worked examples to problem-solving in algebra learning |worked example interleaving]] in Algebra, Noke's [[Bridging_Principles_and_Examples_through_Analogy_and_Explanation |analogical comparison of examples]] in Physics, Renkl's [[Does learning from worked-out examples improve tutored problem solving? |example fading]] in Geometry.

==Experimental support==
See the pages listed in the examples section for studies providing experimental support. See also the references below.

The recent IES practice guide on [http://ies.ed.gov/ncee/wwc/practiceguides/ "Organizing Instruction and Study to Improve Student Learning"] as a great source for relevant references. See particularly the recommendations on interleaving worked examples and multimedia (written primarily by Ken Koedinger).

===Laboratory experiment support===

===In vivo experiment support===

==Theoretical rationale==
Combining examples and rules can enhance [[refinement]] toward better [[feature validity]] of [[knowledge components]]. For example, by prompting students to engage in [[self-explanation]] of an instructional example, students are more likely to try to express the more general rules inherent in the example and thus focus on the deep, relevant features rather than shallow, perceptual features that are irrelevant to correct application of the target [[knowledge component]].

Another way combining instructional examples and rules may enhance [[refinement]] is through [[self-supervised learning]] processes similar to [[co-training]] whereby a learner may draw on complementary strengths and weaknesses of learning by induction from instructional examples versus learning by comprehension of instructional text or rules. More specifically, a learner may identify and eliminate errors in induction from an instructional example by noticing an inconsistency with his or her comprehension of a given verbal rule. Or, conversely, a learner may identify and eliminate errors in comprehension of a rule by noticing an inconsistency with his or her induction (or analogical reasoning) from an example.

==Conditions of application==
==Caveats, limitations, open issues, or dissenting views==
== Variations (descendants) ==

* [[Worked example principle]]
* [[Prompted self-explanation hypothesis]]
** [[Corrective self-explanation]]
* [[Analogical comparison principle]]

==Generalizations (ascendants)==
[[Coordinative Learning]]

==References==
* Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. In Proceedings of Eleventh Annual Conference on Computational Learning Theory (COLT), (pp. 92–100). New York: ACM Press. Available: citeseer.nj.nec.com/blum98combining.html
* Holland, J. H., Holyoak, K. J., Nisbett, R. E., & Thagard, P. R. (1986). Induction: Processes of inference, learning, and discovery. Cambridge, MA: MIT Press.
* Rittle-Johnson, B., Siegler, R. S., & Alibali, M. W. (2001). Developing conceptual understanding and procedural skill in mathematics: An iterative process. Journal of Educational Psychology, 93(2), 346–262.
* Rittle-Johnson, B., & Koedinger, K. R. (2002). Comparing instructional strategies for integrating conceptual and procedural knowledge. Paper presented at the Psychology of Mathematics Education, National, Athens, GA.

References that need to be added:
#worked example references (Sweller, Renkl, etc.)
#specific papers on studies that combine example and rule instruction by Nisbett, Holyoak etc.
#self-explanation references (Chi etc.)
#analogical comparison refs (Gentner, Nokes, etc.)

[[Category:Glossary]]
[[Category:Instructional Principle]]
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English

2011-08-31T13:11:27Z

Pierrehernandez:

The English LearnLab course is described [http://learnlab.org/learnlabs/english/ here].

Numerous studies in the English LearnLab course can be found in the [[Coordinative Learning]], [[Interactive Communication]], and [[Refinement and Fluency]] research clusters.

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