0000000000554096
AUTHOR
Daniela Caballero
Deep Networks for Collaboration Analytics : Promoting Automatic Analysis of Face-to-Face Interaction in the Context of Inquiry-Based Learning
Scholars have applied automatic content analysis to study computer-mediated communication in computer-supported collaborative learning (CSCL). Since CSCL also takes place in face-to-face interactions, we studied the automatic coding accuracy of manually transcribed face-to-face communication. We conducted our study in an authentic higher-education physics context where computer-supported collaborative inquiry-based learning (CSCIL) is a popular pedagogical approach. Since learners’ needs for support in CSCIL vary in the different inquiry phases (orientation, conceptualization, investigation, conclusion, and discussion), we studied, first, how the coding accuracy of five computational models…
English
We propose a method that automatically describes teacher talk. The method allows us to describe and compare classroom lessons, as well as visualizing changes in teacher discourse throughout the course of a lesson. The proposed method uses a machine learning model to infer topics from school textbooks. Certain topics are related to different contents (e.g. kinematics, solar system, electricity), while others are related to different teaching functions (e.g. explanations, questions, numerical exercises). To describe teacher talk, the machine learning method measures the appearance of the inferred topics throughout each lesson. We apply the proposed method to a collection of transcripts from p…
ASR in Classroom Today : Automatic Visualization of Conceptual Network in Science Classrooms
Automatic Speech Recognition (ASR) field has improved substantially in the last years. We are in a point never saw before, where we can apply such algorithms in non-ideal conditions such as real classrooms. In these scenarios it is still not possible to reach perfect recognition rates, however we can already take advantage of these improvements. This paper shows preliminary results using ASR in Chilean and Finnish middle and high school to automatically provide teachers a visualization of the structure of concepts present in their discourse in science classrooms. These visualizations are conceptual networks that relate key concepts used by the teacher. This is an interesting tool that gives…
Conceptual network of teachers' talk : Automatic analysis and quantitative measures
Educational field can take advantage of the improvements of Automatic Speech Recognition (ASR), since we can apply ASR algorithms in non-ideal conditions such as real classrooms. In the context of QuIP project, we used ASR systems to translate audio from teachers’ talk into text to study conceptual networks based on what the teacher says during his/her lecture, particularly the key concepts mentioned and their temporal co-occurrence. In the present study, quantitative metrics are provided, such as centrality measures and PageRank, which can be used to analyse the conceptual networks in a broaden way. With a case-study design, two teachers’ talk are described quantitatively and qualitatively…
Assessing Teacher’s Discourse Effect on Students’ Learning: A Keyword Centrality Approach
The way that content-related keywords co-occur along a lesson seems to play an important role in concept understanding and, therefore, in students’ performance. Thus, network-like structures have been used to represent and summarize conceptual knowledge, particularly in science areas. Previous work has automated the process of producing concept networks, computed different properties of these networks, and studied the correlation of these properties with students’ achievement. This work presents an automated analysis of teachers’ concept graphs, the distribution of relevance amongst content-related keywords and how this affects students’ achievement. Particularly, we automatically extracted…