Dynamic online discussion: task‐oriented interaction for deep learning

2005 ◽  
Vol 42 (3) ◽  
pp. 207-218 ◽  
Author(s):  
Jianxia Du ◽  
Byron Havard ◽  
Heng Li
Keyword(s):  
Deep Learning ◽  
Online Discussion ◽  
Task Oriented

Task-Orientation Online Discussion

2011 ◽  
pp. 154-166 ◽  
Author(s):  
Byron Harvard ◽  
Jianxia Du ◽  
Anthony Olinzock
Keyword(s):  
Deep Learning ◽  
Adaptive Learning ◽  
Cognitive Abilities ◽  
Online Discussion ◽  
Task Orientation ◽  
Multimedia Design ◽  
Effective Interactions ◽  
Online Interactions ◽  
Dynamic Task ◽  
Task Oriented

A dynamic task-oriented online discussion model for deep learning in distance education is described and illustrated in this paper. Information, methods, and cognition, three general learning processes provide the foundation on which the model is based. Three types of online discussion are prescribed; flexible peer, structured topic, and collaborative task discussion. The discussion types are paired with tasks encouraging students to build on their adoptive learning, promoting adaptive learning and challenging their cognitive abilities resulting in deep learning. The online discussion model was applied during two semesters of an online multimedia design for instruction graduate level course. The strategies for creating dynamic discussion serve to facilitate online interactions among diverse learners and assist in the design of assignments for effective interactions. The model proposed and the strategies for dynamic task-oriented discussion provide an online learning environment in which students learn beyond the course goal.

Task-Oriented Online Discussion

2008 ◽  
pp. 1153-1162
Author(s):  
Byron Havard ◽  
Jianxia Du ◽  
Anthony Olinzock
Keyword(s):  
Deep Learning ◽  
Adaptive Learning ◽  
Cognitive Abilities ◽  
Online Discussion ◽  
Learning Processes ◽  
Multimedia Design ◽  
Effective Interactions ◽  
Online Interactions ◽  
Dynamic Task ◽  
Task Oriented

A dynamic task-oriented online discussion model for deep learning in distance education is described and illustrated in this chapter. Information, methods, and cognition, three general learning processes, provide the foundation on which the model is based. Three types of online discussion are prescribed: flexible peer, structured topic, and collaborative task. The discussion types are paired with tasks encouraging students to build on their adoptive learning, promoting adaptive learning and challenging their cognitive abilities, resulting in deep learning. The online discussion model was applied during two semesters of an online multimedia design for instruction graduatelevel course. The strategies for creating dynamic discussion serve to facilitate online interactions among diverse learners and assist in the design of assignments for effective interactions. The model proposed and the strategies for dynamic task-oriented discussion provide an online learning environment in which students learn beyond the course goal.

scholarly journals Using Design-Based Research to Develop Meaningful Online Discussions in Undergraduate Field Experience Courses

2017 ◽  
Vol 18 (6) ◽  
Author(s):  
Carol Johnson ◽  
Laurie Hill ◽  
Jennifer Lock ◽  
Noha Altowairiki ◽  
Christopher Ostrowski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Organizational Design ◽  
Field Experience ◽  
Online Discussion ◽  
Online Discussions ◽  
Task Completion ◽  
Discussion Forums ◽  
Design Based Research ◽  
Online Discussion Forums ◽  
Group Structures

<p class="3">From a design perspective, the intentionality of students to engage in surface or deep learning is often experienced through prescribed activities and learning tasks. Educators understand that meaningful learning can be furthered through the structural and organizational design of the online environment that motivates the student towards task completion. However, learning engagement is unique for each student. It is dependent on both how students learn and their intentions for learning. Based on this challenge, the design of online discussions becomes a pedagogical means in developing students’ intentionality for the adoption of strategies leading to deep learning. Through a Design-Based Research (DBR) approach, iterative design of online learning components for undergraduate field experience courses were studied. For this paper, the focus of the research is on examining factors that influenced deep and surface levels of learning in online discussion forums. The results indicate that design factors (i.e., student engagement, group structures, and organization) influence the nature and degree of deep learning. From the findings, two implications for practice are shared to inform the design and scaffolding of online discussion forums to foster deep approaches to student learning.</p>

scholarly journals A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot

2021 ◽  
Author(s):  
Afia Fairoose Abedin ◽  
Amirul Islam Al Mamun ◽  
Rownak Jahan Nowrin ◽  
Amitabha Chakrabarty ◽  
Moin Mostakim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Service Management ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Emotion Detection ◽  
Named Entity ◽  
Input Text ◽  
Quality Of Products ◽  
Task Oriented

In recent times, a large number of people have been involved in establishing their own businesses. Unlike humans, chatbots can serve multiple customers at a time, are available 24/7 and reply in less than a fraction of a second. Though chatbots perform well in task-oriented activities, in most cases they fail to understand personalized opinions, statements or even queries which later impact the organization for poor service management. Lack of understanding capabilities in bots disinterest humans to continue conversations with them. Usually, chatbots give absurd responses when they are unable to interpret a user’s text accurately. Extracting the client reviews from conversations by using chatbots, organizations can reduce the major gap of understanding between the users and the chatbot and improve their quality of products and services.Thus, in our research we incorporated all the key elements that are necessary for a chatbot to analyse andunderstand an input text precisely and accurately. We performed sentiment analysis, emotion detection, intent classification and named-entity recognition using deep learning to develop chatbots with humanistic understanding and intelligence. The efficiency of our approach can be demonstrated accordingly by the detailed analysis.

Facilitating Deep Learning in a Learning Community

2012 ◽  
Vol 8 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Hea-Jin Lee ◽  
Eun-ok Baek
Keyword(s):  
Deep Learning ◽  
Learning Community ◽  
Online Discussion ◽  
Methods Course ◽  
Mathematics Methods ◽  
Chi Square ◽  
Test Analysis ◽  
Computer Mediated ◽  
Mediated Discourse ◽  
Chi Square Test

The purpose of this study is to explore how the integration of online discussion into a mathematics methods course affected pre-service teachers’ learning. Students’ transcription of online discussion was analyzed using a mixed methods approach, combining computer-mediated discourse analysis and Chi-square test analysis. The data revealed that the online discussion helped pre-service teachers not only deepen their learning of mathematics methods, but also demonstrated their abilities to teach mathematics in different ways. It also indicated that the depth of their learning depended on the levels of threads and topics of discussion. Deep learning occurs 1) more often in the first level thread than subsequent level threads, and 2) in discussion topics, primarily those related to practice-based issues rather than theory-based topics.

Neurolight: A Deep Learning Neural Interface for Cortical Visual Prostheses

2020 ◽  
Vol 30 (09) ◽  
pp. 2050045 ◽  
Author(s):  
Antonio Lozano ◽  
Juan Sebastián Suárez ◽  
Cristina Soto-Sánchez ◽  
Javier Garrigós ◽  
J. Javier Martínez-Alvarez ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
Deep Learning ◽  
Retinal Ganglion Cells ◽  
Computational Models ◽  
Microelectrode Array ◽  
Ganglion Cells ◽  
Semantic Segmentation ◽  
Video Stream ◽  
Retinal Ganglion ◽  
Task Oriented

Visual neuroprosthesis, that provide electrical stimulation along several sites of the human visual system, constitute a potential tool for vision restoration for the blind. Scientific and technological progress in the fields of neural engineering and artificial vision comes with new theories and tools that, along with the dawn of modern artificial intelligence, constitute a promising framework for the further development of neurotechnology. In the framework of the development of a Cortical Visual Neuroprosthesis for the blind (CORTIVIS), we are now facing the challenge of developing not only computationally powerful tools and flexible approaches that will allow us to provide some degree of functional vision to individuals who are profoundly blind. In this work, we propose a general neuroprosthesis framework composed of several task-oriented and visual encoding modules. We address the development and implementation of computational models of the firing rates of retinal ganglion cells and design a tool — Neurolight — that allows these models to be interfaced with intracortical microelectrodes in order to create electrical stimulation patterns that can evoke useful perceptions. In addition, the developed framework allows the deployment of a diverse array of state-of-the-art deep-learning techniques for task-oriented and general image pre-processing, such as semantic segmentation and object detection in our system’s pipeline. To the best of our knowledge, this constitutes the first deep-learning-based system designed to directly interface with the visual brain through an intracortical microelectrode array. We implement the complete pipeline, from obtaining a video stream to developing and deploying task-oriented deep-learning models and predictive models of retinal ganglion cells’ encoding of visual inputs under the control of a neurostimulation device able to send electrical train pulses to a microelectrode array implanted at the visual cortex.

scholarly journals An empirical assessment of deep learning approaches to task-oriented dialog management

2021 ◽  
Vol 439 ◽  
pp. 327-339
Author(s):  
Lukáš Matějů ◽  
David Griol ◽  
Zoraida Callejas ◽  
José Manuel Molina ◽  
Araceli Sanchis
Keyword(s):  
Deep Learning ◽  
Learning Approaches ◽  
Empirical Assessment ◽  
Dialog Management ◽  
Task Oriented
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