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.

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.

scholarly journals Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century

2021 ◽  
Vol 13 (2) ◽  
pp. 800
Author(s):  
Aras Bozkurt ◽  
Abdulkadir Karadeniz ◽  
David Baneres ◽  
Ana Elena Guerrero-Roldán ◽  
M. Elena Rodríguez
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Machine Learning ◽  
Systematic Review ◽  
Deep Learning ◽  
Adaptive Learning ◽  
Research Area ◽  
Learning Processes ◽  
Machine Learning Algorithms ◽  
Research Themes

Artificial intelligence (AI) has penetrated every layer of our lives, and education is not immune to the effects of AI. In this regard, this study examines AI studies in education in half a century (1970–2020) through a systematic review approach and benefits from social network analysis and text-mining approaches. Accordingly, the research identifies three research clusters (1) artificial intelligence, (2) pedagogical, and (3) technological issues, and suggests five broad research themes which are (1) adaptive learning and personalization of education through AI-based practices, (2) deep learning and machine Learning algorithms for online learning processes, (3) Educational human-AI interaction, (4) educational use of AI-generated data, and (5) AI in higher education. The study also highlights that ethics in AI studies is an ignored research area.

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

scholarly journals Vocal Creativity in Elephant Sound Production

Biology ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 750
Author(s):  
Angela S. Stoeger ◽  
Anton Baotic ◽  
Gunnar Heilmann
Keyword(s):  
Cognitive Abilities ◽  
Positive Reinforcement ◽  
Sound Production ◽  
Vocal Learning ◽  
Learning Processes ◽  
Fine Tuning ◽  
Social Feedback ◽  
Nasal Tissue ◽  
Production Techniques ◽  
Production Mechanisms

How do elephants achieve their enormous vocal flexibility when communicating, imitating or creating idiosyncratic sounds? The mechanisms that underpin this trait combine motoric abilities with vocal learning processes. We demonstrate the unusual production techniques used by five African savanna elephants to create idiosyncratic sounds, which they learn to produce on cue by positive reinforcement training. The elephants generate these sounds by applying nasal tissue vibration via an ingressive airflow at the trunk tip, or by contracting defined superficial muscles at the trunk base. While the production mechanisms of the individuals performing the same sound categories are similar, they do vary in fine-tuning, revealing that each individual has its own specific sound-producing strategy. This plasticity reflects the creative and cognitive abilities associated with ‘vocal’ learning processes. The fact that these sounds were reinforced and cue-stimulated suggests that social feedback and positive reinforcement can facilitate vocal creativity and vocal learning behavior in elephants. Revealing the mechanism and the capacity for vocal learning and sound creativity is fundamental to understanding the eloquence within the elephants’ communication system. This also helps to understand the evolution of human language and of open-ended vocal systems, which build upon similar cognitive processes.

scholarly journals Impact of using learning analytics in asynchronous online discussions in higher education

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Juan Pedro Cerro Martínez ◽  
Montse Guitert Catasús ◽  
Teresa Romeu Fontanillas
Keyword(s):  
Learning Analytics ◽  
Participatory Design ◽  
Online Discussion ◽  
Learning Processes ◽  
Online Discussions ◽  
Communication Process ◽  
Control Groups ◽  
Implementation Phase ◽  
Design Based Research ◽  
Asynchronous Online Discussions

Abstract Following asynchronous online discussion activities as a complex communication process is a demanding task for teachers. In this paper, the authors have explored the potential in supporting such activity through learning analytics. From the beginning, the authors acknowledged the limitations of technology to support the complexities of a pedagogical activity. Therefore, the methodology used was participatory design-based research (DBR) divided into two main stages. The first design phase dealt with the engagement of teachers and pedagogical experts in defining the data and metrics to be used to support the pedagogical concepts. The second consisted of an implementation phase including pilots with students and with crucial engagement of teachers in commenting their understanding over students’ learning processes and the feedback the teachers could offer to them. Overall, the students shown improvements in their performance as monitored through the learning analytics group in contrast with control groups. The discussion over the design and its results could be potentially extrapolated to other educational contexts.

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 Artificial Intelligence in Thyroid Field. A Comprehensive Review

Cancers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 4740
Author(s):  
Fabiano Bini ◽  
Andrada Pica ◽  
Laura Azzimonti ◽  
Alessandro Giusti ◽  
Lorenzo Ruinelli ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Cognitive Abilities ◽  
Medical Diagnostics ◽  
Critical Discussion ◽  
Research Field ◽  
Response To Treatment ◽  
Key Concepts ◽  
Mathematical Algorithms

Artificial intelligence (AI) uses mathematical algorithms to perform tasks that require human cognitive abilities. AI-based methodologies, e.g., machine learning and deep learning, as well as the recently developed research field of radiomics have noticeable potential to transform medical diagnostics. AI-based techniques applied to medical imaging allow to detect biological abnormalities, to diagnostic neoplasms or to predict the response to treatment. Nonetheless, the diagnostic accuracy of these methods is still a matter of debate. In this article, we first illustrate the key concepts and workflow characteristics of machine learning, deep learning and radiomics. We outline considerations regarding data input requirements, differences among these methodologies and their limitations. Subsequently, a concise overview is presented regarding the application of AI methods to the evaluation of thyroid images. We developed a critical discussion concerning limits and open challenges that should be addressed before the translation of AI techniques to the broad clinical use. Clarification of the pitfalls of AI-based techniques results crucial in order to ensure the optimal application for each patient.

Learning in multi-team systems: a qualitative study of learning triggers, readiness to learn and learning processes

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Valerie I. Sessa ◽  
Jessica L. Francavilla ◽  
Manuel London ◽  
Marlee Wanamaker
Keyword(s):  
Case Studies ◽  
Learning Process ◽  
Adaptive Learning ◽  
Quantitative Research ◽  
Participant Observation ◽  
Learning Processes ◽  
Future Research ◽  
Content Type ◽  
First Case ◽  
Readiness To Learn

Purpose Multi-team systems (MTSs) are expected to respond effectively to complex challenges while remaining responsive and adaptable and preserving inter-team linking mechanisms. The leadership team of an MTS is expected to configure and reconfigure component teams to meet the unique needs of each situation and perform. How do they learn to do this? This paper, using a recent MTS learning theory as a basis, aims to begin to understand how MTSs learn and stimulate ideas for future research. Design/methodology/approach The authors use two case studies to address research questions. The first case was a snapshot in time, while the second case occurred over several months. Interviews, documents and participant observation were the data sources. Findings As suggested by theory, findings support the idea that learning triggers, the timing of the triggers and readiness to learn (RtL) affect the type of learning process that emerges. The cases showed examples of adaptive and generative team learning. Strong and clear triggers, occurring during performance episodes, led to adaptive learning. When RtL was high and triggers occurred during hiatus periods, the associated learning process was generative. Originality/value Using an available theoretical model and case studies, the research describes how MTS readiness to learn and triggers for learning affect MTS learning processes and how learning outcomes became codified in the knowledge base or structure of the MTS. This provides a framework for subsequent qualitative and quantitative research.

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