scholarly journals Designing an Instrument to Measure Explicit and Implicit Learning Processes

2020 ◽  
Vol 2 (1) ◽  
pp. 92-102
Author(s):  
Anna Zólyomi
Keyword(s):  
Second Language Acquisition ◽  
Secondary School ◽  
Implicit Learning ◽  
Artificial Grammar ◽  
Explicit Learning ◽  
Significant Finding ◽  
Learning Mechanism ◽  
Grammar Learning ◽  
Efl Teachers ◽  
Research Endeavour

In this research paper, the researcher’s intention was to design an instrument that is able to measure learning under two different conditions: explicit and implicit learning. Exploring explicit and implicit learning is gaining more and more attention nowadays in the field of second language acquisition (SLA). The Quantitative method was used in this study to investigate which learning mechanism proves to be more efficient in the selected sample. The present study involved Hungarian technical school, secondary school, and university students from Budapest (N = 40) who participated in completing an Artificial Grammar Learning (AGL) task. The most important finding of the present research endeavour is that implicit learning has proven to be more effective than explicit learning in the case of the selected participants and this was a statistically significant finding. The pedagogical implication of this study is that the effectiveness of implicit learning should be reconsidered by EFL teachers in Hungary.

scholarly journals Learning Simple and Complex Second Language Rules Under Implicit, Incidental, Rule-Search, and Instructed Conditions

1996 ◽  
Vol 18 (1) ◽  
pp. 27-67 ◽  
Author(s):  
Peter Robinson
Keyword(s):  
Second Language ◽  
Second Language Acquisition ◽  
Implicit Learning ◽  
Sentence Type ◽  
Transfer Task ◽  
Explicit Learning ◽  
Complex Stimulus ◽  
Differential Effectiveness ◽  
Computerized Training ◽  
The Relationship

This study examines the generalizability of claims by Reber (1989, 1993) about the implicit learning of artificial grammars to the context of adult second language acquisition (SLA). In the field of SLA Krashen (1981, 1982, 1985, 1994) has made claims parallel to those of Reber regarding the differential effectiveness of conscious learning of rules and unconscious incidental acquisition of rules. Specifically addressed are Reber's and Krashen's claims that (a) implicit learning is more effective than explicit learning when the stimulus domain is complex, and (b) explicit learning of simple and complex stimulus domains is possible if the underlying rules are made salient. One hundred four adult learners of English as a second language were randomly assigned to implicit, incidental, rule-search, or instructed computerized training conditions. Speed and accuracy of judgments of novel tokens of easy and hard rule sentence types presented during training were used as dependent measures. Results do not support the first of Reber's and Krashen's claims but do support the second. Implicit learners do not outperform other learners on complex rules, but instructed learners outperform all others in learning simple rules. Analyses of the effect of sentence type and grammaticality on learning suggest a transfer-appropriate processing account of the relationship among consciousness, rule awareness, training, and transfer task performance.

scholarly journals Characteristics of dissociable human learning systems

1994 ◽  
Vol 17 (3) ◽  
pp. 367-395 ◽  
Author(s):  
David R. Shanks ◽  
Mark F. St. John
Keyword(s):  
Implicit Learning ◽  
Computational Models ◽  
Rule Learning ◽  
Human Learning ◽  
Artificial Grammar Learning ◽  
Learning Systems ◽  
Artificial Grammar ◽  
Stimulus Selection ◽  
Grammar Learning ◽  
Unconscious Learning

AbstractA number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, (1) between learning that takes place with versus without concurrent awareness, and (2) between learning that involves the encoding of instances (or fragments) versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We examine the evidence for implicit learning derived from subliminal learning, conditioning, artificial grammar learning, instrumental learning, and reaction times in sequence learning. We conclude that unconscious learning has not been satisfactorily established in any of these areas. The assumption that learning in some of these tasks (e.g., artificial grammar learning) is predominantly based on rule abstraction is questionable. When subjects cannot report the “implicitly learned” rules that govern stimulus selection, this is often because their knowledge consists of instances or fragments of the training stimuli rather than rules. In contrast to the distinction between conscious and unconscious learning, the distinction between instance and rule learning is a sound and meaningful way of taxonomizing human learning. We discuss various computational models of these two forms of learning.

Measuring Individual Differences in Implicit Learning with Artificial Grammar Learning Tasks

2017 ◽  
Vol 225 (1) ◽  
pp. 5-19 ◽  
Author(s):  
Daniel Danner ◽  
Dirk Hagemann ◽  
Joachim Funke
Keyword(s):  
Individual Differences ◽  
Implicit Learning ◽  
Artificial Grammar Learning ◽  
Artificial Grammar ◽  
General Intelligence ◽  
Knowledge Test ◽  
Learning Tasks ◽  
Grammar Learning ◽  
Grammar Knowledge ◽  
The Impact

Abstract. Implicit learning can be defined as learning without intention or awareness. We discuss conceptually and investigate empirically how individual differences in implicit learning can be measured with artificial grammar learning (AGL) tasks. We address whether participants should be instructed to rate the grammaticality or the novelty of letter strings and look at the impact of a knowledge test on measurement quality. We discuss these issues from a conceptual perspective and report three experiments which suggest that (1) the reliability of AGL is moderate and too low for individual assessments, (2) a knowledge test decreases task consistency and increases the correlation with reportable grammar knowledge, and (3) performance in AGL tasks is independent from general intelligence and educational attainment.

Implicit learning in children with spelling disability: evidence from artificial grammar learning

2012 ◽  
Vol 119 (9) ◽  
pp. 999-1010 ◽  
Author(s):  
Elena Ise ◽  
Carolin J. Arnoldi ◽  
Jürgen Bartling ◽  
Gerd Schulte-Körne
Keyword(s):  
Implicit Learning ◽  
Artificial Grammar Learning ◽  
Artificial Grammar ◽  
Grammar Learning ◽  
Spelling Disability

An Event-related fMRI Study of Artificial Grammar Learning in a Balanced Chunk Strength Design

2004 ◽  
Vol 16 (3) ◽  
pp. 427-438 ◽  
Author(s):  
Matthew D. Lieberman ◽  
Grace Y. Chang ◽  
Joan Chiao ◽  
Susan Y. Bookheimer ◽  
Barbara J. Knowlton
Keyword(s):  
Functional Connectivity ◽  
Implicit Learning ◽  
Temporal Lobe ◽  
Medial Temporal Lobe ◽  
Artificial Grammar Learning ◽  
Artificial Grammar ◽  
Sources Of Information ◽  
Grammar Learning ◽  
Rule System ◽  
Chunk Strength

Artificial grammar learning (Reber, 1967) is a form of implicit learning in which cognitive, rather than motor, implicit learning has been found. After viewing a series of letter strings formed according to a finite state rule system, people are able to classify new letter strings as to whether or not they are formed according to these grammatical rules despite little conscious insight into the rule structure. Previous research has shown that these classification judgments are based on knowledge of abstract rules as well as superficial similarity (“chunk strength”) to training strings. Here we used event-related fMRI to identify neural regions involved in using both sources of information as test stimuli were designed to unconfound chunk strength from rule use. Using functional connectivity analyses, the extent to which the sources of information are complementary or competitive was also assessed. Activation in the right caudate was associated with rule adherence, whereas medial temporal lobe activations were associated with chunk strength. Additionally, functional connectivity analyses revealed caudate and medial temporal lobe activations to be strongly negatively correlated (r = −88) with one another during the performance of this task.

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