Analyzing Discourse and Text Complexity for Learning and by Mihai Dascălu

Analyzing Discourse and Text Complexity for Learning and by Mihai Dascălu

By Mihai Dascălu

With the appearance and lengthening approval for desktop Supported Collaborative studying (CSCL) and e-learning applied sciences, the necessity of automatic evaluate andof teacher/tutor support for the 2 tightly intertwined actions of comprehension of studying fabrics and of collaboration between friends has grown considerably. during this context, a polyphonic version of discourse derived from Bakhtin’s paintings as a paradigm is used for reading either normal texts and CSCL conversations in a distinct framework involved in various points of textual cohesion.

As specificity of our research, the individual learning standpoint is concentrated at the id of interpreting ideas and on offering a multi-dimensional textual complexity version, while the collaborative learning size is established at the overview of individuals’ involvement, in addition to on collaboration assessment.

Our procedure according to complicated typical Language Processing suggestions offers a qualitative estimation of the training approach and complements knowing as a “mediator of studying” via delivering computerized suggestions to either inexperienced persons and lecturers or tutors. the most advantages are its flexibility, extensibility and however specificity for masking a number of levels, ranging from interpreting lecture room fabrics, to discussing on particular subject matters in a collaborative demeanour and completing the suggestions loop by way of verbalizing metacognitive thoughts.

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Additional info for Analyzing Discourse and Text Complexity for Learning and Collaborating: A Cognitive Approach Based on Natural Language Processing

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In terms of cleaning, SourceRater requires proper paragraph markings and automatically detects non-standard characters, certain punctuation, and erroneous end-of-sentence markers (Nelson et al. 2012). Word Maturity (Landauer et al. 2011), introduced in the Pearson Reading Maturity Metric, uses language models built using Latent Semantic Analysis (LSA) (Landauer et al. 2 Semantic Similarity through Tagged LSA) to estimate how much language experience is required to achieve the adult meaning of each word, sentence and paragraph within a given text (Kireyev and Landauer 2011).

In informal learning, discussions between the members of communities of practice often take place using chats. Nevertheless, instant messaging determined a change in the way collaborative work is regarded, becoming a viable alternative to the classic view of learning: Computer Supported Collaborative Learning (CSCL) (Stahl 2006b) that advocates for the use of chats as a supplement for standard teaching and learning strategies (Stahl 2006b). In this manner, chat has been introduced in formal education as well and is used by students to solve problems and debate difficult topics in order to develop their knowledge about a given domain and to learn from their peers.

2007), it is easier to identify paraphrases and irrelevant explanations, but it is more difficult to identify and evaluate verbalizations that contain information coming from students’ experience. From a completely different point of view, Zhang et al. edu/~listen/). The approach used a logistic regression model on both synthetic and authentic data that consisted of tutorials, transcribed spoken responses, expert annotations and rationales for the recommendations in terms of features of the student responses.

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