4. The effectiveness of corrective feedback for the acquisition of L2 grammar

2006 ◽  
pp. 133-164 ◽  
Author(s):  
Jane Russell Valezy ◽  
Nina Spada
Keyword(s):  
Corrective Feedback

Implicit and explicit corrective feedback and feedback exposure conditions

2020 ◽  
Vol 4 (1) ◽  
pp. 3-48
Author(s):  
Takehiro Iizuka ◽  
Kimi Nakatsukasa
Keyword(s):  
Explicit Knowledge ◽  
Corrective Feedback ◽  
Significant Difference ◽  
Oral Corrective Feedback ◽  
Post Test ◽  
Picture Description ◽  
Potential Factors ◽  
Implicit And Explicit ◽  
Instructional Settings ◽  
The Impact

This exploratory study examined the impact of implicit and explicit oral corrective feedback (CF) on the development of implicit and explicit knowledge of Japanese locative particles (activity de, movement ni and location ni) for those who directly received CF and those who observed CF in the classroom. Thirty-six college students in a beginning Japanese language course received either recast (implicit), metalinguistic (explicit) or no feedback during an information-gap picture description activity, and completed a timed picture description test (implicit knowledge) and an untimed grammaticality judgement test (explicit knowledge) in a pre-test, immediate post-test and delayed post-test. The results showed that overall there was no significant difference between CF types, and that CF benefited direct and indirect recipients similarly. Potential factors that might influence the effectiveness of CF, such as instructional settings, complexity of target structures and pedagogy styles, are discussed.

scholarly journals Computer Adaptive Testing Using Upper-Confidence Bound Algorithm for Formative Assessment

2019 ◽  
Vol 9 (20) ◽  
pp. 4303 ◽  
Author(s):  
Jaroslav Melesko ◽  
Vitalij Novickij
Keyword(s):  
Formative Assessment ◽  
Latent Trait ◽  
Corrective Feedback ◽  
Strong Support ◽  
Adaptive Testing ◽  
Computer Adaptive Testing ◽  
Confidence Bound ◽  
Question Item ◽  
E Learning ◽  
Upper Confidence Bound

There is strong support for formative assessment inclusion in learning processes, with the main emphasis on corrective feedback for students. However, traditional testing and Computer Adaptive Testing can be problematic to implement in the classroom. Paper based tests are logistically inconvenient and are hard to personalize, and thus must be longer to accurately assess every student in the classroom. Computer Adaptive Testing can mitigate these problems by making use of Multi-Dimensional Item Response Theory at cost of introducing several new problems, most problematic of which are the greater test creation complexity, because of the necessity of question pool calibration, and the debatable premise that different questions measure one common latent trait. In this paper a new approach of modelling formative assessment as a Multi-Armed bandit problem is proposed and solved using Upper-Confidence Bound algorithm. The method in combination with e-learning paradigm has the potential to mitigate such problems as question item calibration and lengthy tests, while providing accurate formative assessment feedback for students. A number of simulation and empirical data experiments (with 104 students) are carried out to explore and measure the potential of this application with positive results.

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