"discourse features" is a correct and usable phrase in written English.
It refers to the various linguistic elements and strategies used in spoken or written communication to convey meaning, such as tone, rhythm, word choice, and syntactic structure. Here's an example sentence: "The politician's use of sarcasm and repetition were effective discourse features in his speech, captivating the audience and driving home his main points."
The course lays out the main stages of analyzing language to understand social dynamics, including using theory to identify key discourse features, feature extraction, and classification and prediction.
The findings revealed an overall positive attitude towards writing in English, although difficulties with specific discourse features and with the most challenging sections and aspects of the RA were also identified.
The adopted approach applies comprehensive analysis of language and discourse features associated with the specificity of students' internal self-feedback, which is externalized as self-reflections in video annotations.
It aims to shed light on (a) discourse features of classroom digital talk, (b) the social and cultural contexts that mediate it, (c) online and offline practices that influence it, and (d) relationships between types of digital talk and types of learning.
With the popularity of information and communication technologies (ICTs) in classrooms and the increasing traction of digital talk in schools, the project studies not only discourse features of digital talk, but also the afforded communicative possibilities for learning and the ways in which its use is organized.
The course will draw on action-oriented language approaches to understand how people use language (e.g., grounding and joint action models), and then build on this approach to understand how discourse features from natural language can be used to answer questions from a wide range of social science questions, and ultimately, to the design of new technologies.
The discourse feature extraction relies on the same automatic speech recognition module as described in the lexical features (Lexical features).
Yet a fourth explanation might simply be that CACs are a common discourse feature in history texts.
We choose from 1-word to 5-word repetitions and formulate the dimensions of discourse feature set as: number of 1-word repetition, number of 2-word repetitions, and likewise.
While earth sciences and mathematics seem to share some features of knowledge construction practices with other disciplines in the sciences, Bernstein and Kuteeva and Airey also note the uniqueness of the mathematics discourse featured by its distinct symbolic codes and reasoning style (e.g., mathematical proofs) rather than empirical data analyses.
Figure 4 Classification accuracy for different combinations of affective sources (A = Acoustic, L = Lexical, D = Discourse), feature selection schemes ( Base, f10, f15, PCA ) and different classification techniques of (a) xtitSVM, (b) SVM-5WC, (c) SVM-10WC, (d) SVM - 5CV, (e) kNN, and (f) k NN- 5CV.
