| 1 |
La retroacció correctiva oral amb estudiants adults poc escolaritzats
|
|
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Lost in Uptake Translation: Examining Genre Negotiations in Students’ Writing Performances
|
|
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Local Histories of Composition and the Student Writer: Women Students Writing Within, Against, and Beyond Required Classroom Genres
|
|
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Writing Assignments and Student Responses: Uptake in a Fifth-Grade Class
|
|
|
|
BASE
|
|
Show details
|
|
|
| 5 |
EXPLAINING SELECTION: EXAMINING UPTAKE IN THEORY AND LITERATURE
|
|
|
|
BASE
|
|
Show details
|
|
|
| 6 |
The Role of Prompts as Focus on Form on Uptake
|
|
|
|
In: Open Access Dissertations (2011)
|
|
BASE
|
|
Show details
|
|
|
| 7 |
The provision and uptake of different types of recasts in child and adult ESL learners: what is the role of age and context?
|
|
|
|
BASE
|
|
Show details
|
|
|
| 8 |
Disrupting Conventions: When and Why Writers Take Up Innovation
|
|
|
|
BASE
|
|
Show details
|
|
|
| 9 |
SEMELHANÇAS ENTRE UPTAKE E TRACE: CONSIDERAÇÕES SOBRE TRADUÇÃO
|
|
|
|
In: DELTA: Documentação de Estudos em Lingüística Teórica e Aplicada, Vol 13, Iss 2, Pp 315-329 (1997) (1997)
|
|
BASE
|
|
Show details
|
|
|
| 10 |
Domain Independent Assessment of Dialogic Properties of Classroom Discourse
|
|
|
|
In: http://educationaldatamining.org/EDM2014/uploads/procs2014/short+papers/233_EDM-2014-Short.pdf
|
|
Abstract:
We present a machine learning model that uses particular attributes of individual questions asked by teachers and students to predict two properties of classroom discourse that have previously been linked to improved student achievement. These properties, uptake and authenticity, have previously been studied by using trained observers to live-code classroom instruction. As a first-step in automating the coding of classroom discourse, we model question properties based on the features of individual questions, without any information about the context or domain. We then compare the machine-coded results to two referents: human-coded individual questions and “gold standard ” codes from existing data. The performance achieved by the models is as good as human experts on the comparable task of coding individual questions out of context. Yet ultimately, this study highlights the need to draw on contextualizing information in order to most completely identify question properties associated with individual questions.
|
|
Keyword:
Authenticity; Machine Learning; Uptake
|
|
URL: http://educationaldatamining.org/EDM2014/uploads/procs2014/short+papers/233_EDM-2014-Short.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.661.826
|
|
BASE
|
|
Hide details
|
|
|
| 11 |
Students ’ Uptake of Corrective Feedback
|
|
|
|
In: http://mcser.org/images/stories/JESR-Special-Issues/JESR+2012+Special+Issue+vol+2+no+7/Ataisi+Emiya+Gladday.pdf
|
|
BASE
|
|
Show details
|
|
|
|
|
