Think Tank Methods for Group Decision Making

Group Decision-making is probably preferable to individual decision making in many cases.. There are three techniques that when used properly (brainstorming, the Delphi process, nominal group technique) have been extremely useful for increasing the creative capacity of the group in generating ideas, understanding the problems and take better decisions. Practical considerations, of course, often influence the technique used. For example, factors such as the number of available work hours, costs and physical proximity of the participants influence the choice of one technique or another.

Delphi Process

This technique consist on requesting and comparing anonymous judgments on the subject of interest through a series of sequential questionnaires applied to experts in the subject. The Delphi process preserves the advantage of obtaining a diversity of judgments, opinions and approaches while suppressing biases that can occur during the face to face interaction. The predictive power of Delphi is based on the systematic use of intuitive judgment given by a panel of experts. It requires a massive turnout for the results to be statistically significant. But the group must have a high degree of correspondence with the topics to be covered in the exercise.

Braingstorming

Brainstorming is widely used in the advertising sector, where apparently quite effective. In other fields, he has been less successful. The basic rules are:

1. No idea is too ridiculous. Group members are encouraged to express extreme or crazy ideas.
2. Each idea presented belongs to the group, not the person exposed. In this way, the group can use them and build on the ideas of others.
3. No idea can be criticized. The purpose of the meeting is to generate not evaluate ideas.

Nominal Group

According to Tamella (2013), basically, the nominal group technique is a structured group meeting which seven to ten people sit around a table without speaking to each other. Each person writes down ideas in a notebook paper. After five minutes, a structured exchange of ideas is performed. Each person has an idea. A person appointed as secretary writes the ideas on a flipchart or whiteboard in view of the whole group. This continues until all participants indicate that there are more ideas to share. The next stage is the formal debate in which each idea receives attention and debate before being voted on. This is accomplished by asking for clarification or indicating the degree of support for each idea on the flipchart. The last step is the independent vote in which each participant privately selects priorities ranking or vote. The group's decision is the result mathematically ordered individual votes.

Van der Ven (1974) states that both the Delphi method and nominal group technique have shown more efficient than brainstorming.  He also points that the basic differences between the Delphi method and nominal group technique are:

1. Participants in the Delphi process are usually anonymous to each other, while participating in a nominal group are known.
2. Participants of a nominal group face to face around a table are, while participants of a Delphi process are physically distant and never meet.
3. In the Delphi process, all communications between participants are through written questionnaires and comments by supervisory personnel. In the nominal group participants communicate directly.

References

Tammela, O. (2013), Applications of consensus methods in the improvement of care of paediatric patients: a step forward from a ‘good guess’. Acta Paediatrica, 102: 111–115. doi: 10.1111/apa.12120

Van der Ven, A. (1974). The Effectiveness of Nominal, Delphi, and Interacting Group Decision Making Processes. Academy of Management Journal. doi: 10.2307/255641

Learning Analytics

According to Johnson, Adams Becker, Cummins & Estrada (2014), learning analytics is the interpretation of a wide range of data produced and gathered about students to guide their academic progress, predict future actions and identify problematic elements. The aim of the collection, recording, analysis and presentation of these data is to enable teachers to adapt quickly and effectively educational strategies at the level and ability demanded by each student. Even in its early stages of development, learning analytics often respond to the need to carry out monitoring and control activities in the virtual campus for strategic decision making. In addition, also seek to exploit the vast amount of data produced by students in academic activities.

Learning Analytics

Overall, the information provided to customize the training and learning environments designed according to the needs, interests and ways of interaction of teachers and students. The statistical record of the activity of students and teachers also identify hot spots of a teaching-learning process.

Even though experts predict that this technology should be massively adopted in 3 to 5 years, the potential of learning analytics has its obstacles. Indeed, the privacy of student data is an important issue that has received attention recently, but there are others. One of these challenges is to broaden the perspective of educators about the possibilities of a personalized learning guided by analytics. This marks a significant difference from the instructional strategies guided by traditional data. This is because there are many more data available to extract information, make sense and usefulness.

Among the forces impacting this technological trend, we can find the increasingly blurred boundaries between formal and informal learning. This means that the same person may be participating in a course of a virtual campus, following a series of twitters and blogs, communicate in forums with fellow students and synchronously with friends and colleagues, etc. Buckingam and Ferguson (2011) point out that the use of digital fingerprinting can be applied to a wide variety of contexts and allow analyzing the behavior in a wide variety of situations.

Computer-assisted instruction is not new, but the proliferation of technology and sophisticated methods for data analysis are. The quality and quantity of available data opens new opportunities to provide effective personalized learning experience, but it certainly some challenges. However, we have seen firsthand the benefits that these technologies can have on millions of students, so we think it is a journey worth doing.

References

Ballard, C. (2012). Learning Analytics - Improving Student Retention. Retrieved from http://www.slideshare.net/ChrisBallard/learning-analytics-improving-student-retention

Buckingham, S. & Ferguson, R. (2011). Social Learning Analytics. Technical Report KMI-11-01, Knowledge Media Institute, The Open University, UK. Retrieved from http://kmi.open.ac.uk/publications/pdf/kmi-11-01.pdf

Johnson, L., Adams Becker, S., Cummins, M., and Estrada, V. (2014). 2014 NMC Technology Outlook for International Schools in Asia: A Horizon Project Regional Report. Austin, Texas: The New Media Consortium.

About Emerging Technologies and ET ROOM

ABOUT THIS BLOG

Innovation and Emerging Technologies are the only way to ensure that companies remain competitive. But this is not easy, since there is a big difference between talking about innovation and putting it into practice. Innovation is a bet on the future. I am looking forward to discuss the various ways and various areas where innovation can contribute in our organizations.

Emerging technologies are defined as "scientific innovations" that can create a new industry or transform an existing one. They include discontinuous technologies derived from radical innovations as well as more advanced technologies formed as a result of the convergence of branches of previously separate research.

Each of these technologies offers a rich range of market opportunities that provide incentive for investment risk . The problem with these new technologies, both managers of mature firms and those of start-ups is that traditional management tools are not able to solve successfully the new challenges generated.

They are used interchangeably to indicate the emergence and convergence of new technologies with demonstrated potential as disruptive technologies terms. Among them are nanotechnology, biotechnology, information and communications technology, cognitive science, robotics, and artificial intelligence.

Precisely what “the future” consists of is unknown, or at best uncertain. Certain emerging technologies will enable the development of other new technologies. In this sense, some technologies are enabling technologies that allow other technologies to be more fully exploited. (Koty, 2006).

ABOUT THE AUTHOR

Pedro Taveras received a bachelor’s degree from the Pontificia Universidad Católica Madre y Maestra, Dominican Republic, in systems and computing engineering (CUM LAUDE), he successfully completed the coursework for the master of science degree Information & Telecommunications Technologies from Rochester Institute of Technology in May 2001 and a master's degree in Information Systems from Stevens Institute of Technology with concentration in technology management. He worked as a Software Engineer for the PCA Group, a technology leader firm with markets in Buffalo, NY, Rochester, NY and Kansas City, KS. He lectures  Information and Computer Science related subects at Pontificia Universidad Catolica Madre y Maestra. He is CEO-Founder and Software Architect for Xtudia, LLC, a software development firm that produces solutions for industrial automation, business and education. His research interests include software engineering, information processing, knowledge management systems; 2d/3d programming, Human-Computer Interaction, Human Aspects of Computing, natural end user interface and pervasive (ubiquitous) computing. He is currently enrolled in the second year of th Doctoral Program in Computer Science with a concentration in Information Assurance at Colorado Technical University.

Interest: information security, software architecture,  software engineering, information processing, knowledge management systems; 2d/3d programming, Human-Computer Interaction, Human Aspects of Computing, natural end user interface and pervasive (ubiquitous) computing.

References

Koty, W.(2006). Ahead of the future. Report on emerging technology research. Retrieved from http://www.gov.bc.ca/premier/attachments/Emerging_Tech_Research.pdf