Discussoo: Towards an intelligent tool for multi-scale participatory modeling

Abstract In participatory modeling (PM), a conceptual model emerges from an exchange of information and opinions among stakeholders. This usually happens in a series of in-person workshops restricted to a certain number of attendees during designated time intervals. Our goal is to open up the PM workshop process to engage an unlimited number of participants at various locations, while supporting them with the functionality that the modeling context can offer. We develop a real-time, moderated steering environment, named Discussoo, to facilitate online PM. Users express their opinions about a topic by providing their comments in online discussions. As the discussion evolves, an ensemble of artificial intelligence algorithms in the background automatically produces a dynamic conceptual model to visualize the on-going exchange of opinions. Moderators can use this model to provide feedback to users and guide the discussion. Policymakers and managers can use Discussoo to support more transparent and meaningful engagement of stakeholders.

[1]  Anselmo Peñas,et al.  Supporting scientific knowledge discovery with extended, generalized Formal Concept Analysis , 2016, Expert Syst. Appl..

[2]  Alyssa Friend Wise,et al.  Mining for gold: Identifying content-related MOOC discussion threads across domains through linguistic modeling , 2017, Internet High. Educ..

[3]  Philippe J. Giabbanelli,et al.  Twelve Questions for the Participatory Modeling Community , 2018, Earth's Future.

[4]  Pierre Dillenbourg,et al.  Semiautomatic Annotation of MOOC Forum Posts , 2016 .

[5]  Poorva Agrawal,et al.  Named Entity Recognition Approaches and Their Comparison for Custom NER Model , 2020 .

[6]  Andrew A. Tawfik,et al.  Detecting the Depth and Progression of Learning in Massive Open Online Courses by Mining Discussion Data , 2020, Technology, Knowledge and Learning.

[7]  Aditya Johri,et al.  Needle in a haystack: Identifying learner posts that require urgent response in MOOC discussion forums , 2018, Comput. Educ..

[8]  Kiran Adnan,et al.  An analytical study of information extraction from unstructured and multidimensional big data , 2019, Journal of Big Data.

[9]  Alan L. Porter,et al.  Clustering scientific documents with topic modeling , 2014, Scientometrics.

[10]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[11]  R. Srivastava,et al.  Machine Learning Techniques for Sentiment Analysis: A Review , 2020 .

[12]  Katashi Nagao,et al.  Discussion Mining: Annotation-Based Knowledge Discovery from Real World Activities , 2004, PCM.

[13]  N. Videira,et al.  Engaging Stakeholders in Environmental and Sustainability Decisions with Participatory System Dynamics Modeling , 2017 .

[14]  Omprakash Gnawali,et al.  Language independent analysis and classification of discussion threads in Coursera MOOC forums , 2014, Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014).

[15]  Yuen-Hsien Tseng,et al.  Mining concept maps from news stories for measuring civic scientific literacy in media , 2010, Comput. Educ..

[16]  Mario Milicevic,et al.  The automatic creation of concept maps from documents written using morphologically rich languages , 2012, Expert Syst. Appl..

[17]  James Bailey,et al.  Identifying At-Risk Students in Massive Open Online Courses , 2015, AAAI.

[18]  W. Bruce Croft,et al.  LDA-based document models for ad-hoc retrieval , 2006, SIGIR.

[19]  António Moreira,et al.  Assessing social construction of knowledge online: A critique of the interaction analysis model , 2014, Comput. Hum. Behav..

[20]  Cécile Barnaud,et al.  Equity, Power Games, and Legitimacy: Dilemmas of Participatory Natural Resource Management , 2013 .

[21]  Linda Corrin,et al.  Predicting success: how learners' prior knowledge, skills and activities predict MOOC performance , 2015, LAK.

[22]  Richard N. Palmer,et al.  An interdisciplinary framework for participatory modeling design and evaluation—What makes models effective participatory decision tools? , 2017 .

[23]  Rafael A. Calvo,et al.  Concept Map Mining: A Definition and a Framework for Its Evaluation , 2008, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[24]  Philippe J. Giabbanelli,et al.  Should we simulate mental models to assess whether they agree? , 2018, SpringSim.

[25]  Bing Liu,et al.  Aspect and Entity Extraction for Opinion Mining , 2014 .

[26]  Philippe J. Giabbanelli,et al.  Feasibility and Framing of Interventions Based on Public Support: Leveraging Text Analytics for Policymakers , 2016, HCI.

[27]  Adil Rajput,et al.  Natural Language Processing, Sentiment Analysis and Clinical Analytics , 2019, Innovation in Health Informatics.

[28]  Sondoss El Sawah,et al.  An empirical investigation into the learning effects of management flight simulators: A mental models approach , 2017, Eur. J. Oper. Res..

[29]  Jerome R. Ravetz,et al.  Uncertainty, complexity and post-normal science , 1994 .

[30]  Albrecht Fortenbacher,et al.  Predicting students' success based on forum activities in MOOCs , 2015, 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS).

[31]  Frank H. Maier,et al.  What are we talking about?—A taxonomy of computer simulations to support learning , 2000 .

[32]  Ani Nenkova,et al.  A Survey of Text Summarization Techniques , 2012, Mining Text Data.

[33]  Philippe J. Giabbanelli,et al.  Iterative generation of insight from text collections through mutually reinforcing visualizations and fuzzy cognitive maps , 2019, Appl. Soft Comput..

[34]  Philippe J. Giabbanelli,et al.  The Artificial Facilitator: Guiding Participants in Developing Causal Maps Using Voice-Activated Technologies , 2019, HCI.