Matching Items (4)

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Spoken dialogue in face-to-face and remote collaborative learning environments

Description

Research in the learning sciences suggests that students learn better by collaborating with their peers than learning individually. Students working together as a group tend to generate new ideas more

Research in the learning sciences suggests that students learn better by collaborating with their peers than learning individually. Students working together as a group tend to generate new ideas more frequently and exhibit a higher level of reasoning. In this internet age with the advent of massive open online courses (MOOCs), students across the world are able to access and learn material remotely. This creates a need for tools that support distant or remote collaboration. In order to build such tools we need to understand the basic elements of remote collaboration and how it differs from traditional face-to-face collaboration.

The main goal of this thesis is to explore how spoken dialogue varies in face-to-face and remote collaborative learning settings. Speech data is collected from student participants solving mathematical problems collaboratively on a tablet. Spoken dialogue is analyzed based on conversational and acoustic features in both the settings. Looking for collaborative differences of transactivity and dialogue initiative, both settings are compared in detail using machine learning classification techniques based on acoustic and prosodic features of speech. Transactivity is defined as a joint construction of knowledge by peers. The main contributions of this thesis are: a speech corpus to analyze spoken dialogue in face-to-face and remote settings and an empirical analysis of conversation, collaboration, and speech prosody in both the settings. The results from the experiments show that amount of overlap is lower in remote dialogue than in the face-to-face setting. There is a significant difference in transactivity among strangers. My research benefits the computer-supported collaborative learning community by providing an analysis that can be used to build more efficient tools for supporting remote collaborative learning.

Contributors

Agent

Created

Date Created
  • 2014

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Using differential sequence mining to associate patterns of interactions in concept mapping activity with dimensions of collaborative process

Description

Computer supported collaborative learning (CSCL) has made great inroads in classroom teaching marked by the use of tools and technologies to support and enhance collaborative learning. Computer mediated learning environments

Computer supported collaborative learning (CSCL) has made great inroads in classroom teaching marked by the use of tools and technologies to support and enhance collaborative learning. Computer mediated learning environments produce large amounts of data, capturing student interactions, which can be used to analyze students’ learning behaviors (Martinez-Maldonado et al., 2013a). The analysis of the process of collaboration is an active area of research in CSCL. Contributing towards this area, Meier et al. (2007) defined nine dimensions and gave a rating scheme to assess the quality of collaboration. This thesis aims to extract and examine frequent patterns of students’ interactions that characterize strong and weak groups across the above dimensions. To achieve this, an exploratory data mining technique, differential sequence mining, was employed using data from a collaborative concept mapping activity where collaboration amongst students was facilitated by an interactive tabletop. The results associate frequent patterns of collaborative concept mapping process with some of the dimensions assessing the quality of collaboration. The analysis of associating these patterns with the dimensions of collaboration is theoretically grounded, considering aspects of collaborative learning, concept mapping, communication, group cognition and information processing. The results are preliminary but still demonstrate the potential of associating frequent patterns of interactions with strong and weak groups across specific dimensions of collaboration, which is relevant for students, teachers, and researchers to monitor the process of collaborative learning. The frequent patterns for strong groups reflected conformance to the process of conversation for dimensions related to “communication” aspect of collaboration. In terms of the concept mapping sub-processes the frequent patterns for strong groups reflect the presentation phase of conversation with processes like talking, sharing individual maps while constructing the groups concept map followed by short utterances which represents the acceptance phase. For “joint information processing” aspect of collaboration, the frequent patterns for strong groups were marked by learners’ contributing more upon each other’s work. In terms of the concept mapping sub-processes the frequent patterns were marked by learners adding links to each other’s concepts or working with each other’s concepts, while revising the group concept map.

Contributors

Agent

Created

Date Created
  • 2015

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Using the tablet gestures and speech of pairs of students to classify their collaboration

Description

This thesis is an initial test of the hypothesis that superficial measures suffice for measuring collaboration among pairs of students solving complex math problems, where the degree of collaboration

This thesis is an initial test of the hypothesis that superficial measures suffice for measuring collaboration among pairs of students solving complex math problems, where the degree of collaboration is categorized at a high level. Data were collected

in the form of logs from students' tablets and the vocal interaction between pairs of students. Thousands of different features were defined, and then extracted computationally from the audio and log data. Human coders used richer data (several video streams) and a thorough understand of the tasks to code episodes as

collaborative, cooperative or asymmetric contribution. Machine learning was used to induce a detector, based on random forests, that outputs one of these three codes for an episode given only a characterization of the episode in terms of superficial features. An overall accuracy of 92.00% (kappa = 0.82) was obtained when

comparing the detector's codes to the humans' codes. However, due irregularities in running the study (e.g., the tablet software kept crashing), these results should be viewed as preliminary.

Contributors

Agent

Created

Date Created
  • 2014

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Interactive laboratory for digital signal processing in iOS devices

Description

The demand for handheld portable computing in education, business and research has resulted in advanced mobile devices with powerful processors and large multi-touch screens. Such devices are capable of handling

The demand for handheld portable computing in education, business and research has resulted in advanced mobile devices with powerful processors and large multi-touch screens. Such devices are capable of handling tasks of moderate computational complexity such as word processing, complex Internet transactions, and even human motion analysis. Apple's iOS devices, including the iPhone, iPod touch and the latest in the family - the iPad, are among the well-known and widely used mobile devices today. Their advanced multi-touch interface and improved processing power can be exploited for engineering and STEM demonstrations. Moreover, these devices have become a part of everyday student life. Hence, the design of exciting mobile applications and software represents a great opportunity to build student interest and enthusiasm in science and engineering. This thesis presents the design and implementation of a portable interactive signal processing simulation software on the iOS platform. The iOS-based object-oriented application is called i-JDSP and is based on the award winning Java-DSP concept. It is implemented in Objective-C and C as a native Cocoa Touch application that can be run on any iOS device. i-JDSP offers basic signal processing simulation functions such as Fast Fourier Transform, filtering, spectral analysis on a compact and convenient graphical user interface and provides a very compelling multi-touch programming experience. Built-in modules also demonstrate concepts such as the Pole-Zero Placement. i-JDSP also incorporates sound capture and playback options that can be used in near real-time analysis of speech and audio signals. All simulations can be visually established by forming interactive block diagrams through multi-touch and drag-and-drop. Computations are performed on the mobile device when necessary, making the block diagram execution fast. Furthermore, the extensive support for user interactivity provides scope for improved learning. The results of i-JDSP assessment among senior undergraduate and first year graduate students revealed that the software created a significant positive impact and increased the students' interest and motivation and in understanding basic DSP concepts.

Contributors

Agent

Created

Date Created
  • 2011