Automating the process of identifying the preferred representational system in Neuro Linguistic Programming using Natural Language Processing

Voice and Communication Training Program Improves Performance of University Students in Oral Presentations

PURPOSE

To investigate the effect of a voice and communication training program for oral presentations on higher education students.

METHODS

The proposed training program was based on the areas of social skills, voice projection techniques, and neurolinguistic programming. Thirty-eight students participated in the training with active learning methodologies at the university. Before and after the intervention, the participants recorded a short oral presentation on a topic of their choice. The recording was presented to the other participants and to a panel formed by three examiners (two articulation therapists and a psychologist), who evaluated the oral presentation performances. Moreover, each individual self-assessed their communication. The evaluation criteria covered the linguistic aspects, formal and non-formal, verbal and non-verbal communication, planning, and elaboration of the presentation.

RESULTS

All participants improved their performance in oral presentations regarding verbal and para-verbal aspects, ability to keep the audience, emotional control, planning, objective, content, approach, organization, visual resource, form of presentation, language, and general elements (general presentation).

CONCLUSION

The proposed training program is effective in improving the performance of university students in oral presentations.

A Machine Learning Approach to Identify the Preferred Representational System of a Person

Whenever people think about something or engage in activities, internal mental processes will be engaged. These processes consist of sensory representations, such as visual, auditory, and kinesthetic, which are constantly being used, and they can have an impact on a person’s performance. Each person has a preferred representational system they use most when speaking, learning, or communicating, and identifying it can explain a large part of their exhibited behaviours and characteristics. This paper proposes a machine learning-based automated approach to identify the preferred representational system of a person that is used unconsciously. A novel methodology has been used to create a specific labelled conversational dataset, four different machine learning models (support vector machine, logistic regression, random forest, and k-nearest neighbour) have been implemented, and the performance of these models has been evaluated and compared. The results show that the support vector machine model has the best performance for identifying a person’s preferred representational system, as it has a better mean accuracy score compared to the other approaches after the performance of 10-fold cross-validation. The automated model proposed here can assist Neuro Linguistic Programming practitioners and psychologists to have a better understanding of their clients’ behavioural patterns and the relevant cognitive processes. It can also be used by people and organisations in order to achieve their goals in personal development and management. The two main knowledge contributions in this paper are the creation of the first labelled dataset for representational systems, which is now publicly available, and the use of machine learning techniques for the first time to identify a person’s preferred representational system in an automated way.

Identification of Preferred Representational Sensory System in Neuro-Linguistic Programming

The paper highlights the scientific debate on the Neurolinguistic programming (NLP) issue. NLP is a collection of approaches, communication tools, techniques, and perspectives that determine how individuals think and communicate through language. NLP is used to recognize and modify patterns of human behavior. The sensory representational system, or the method for recognizing representational systems, which is made up of five main senses, influences this process. Systematization literary sources and approaches to this issue indicate that three sensory representational systems exist in the NLP approach: visual, auditory, and kinesthetic (VAK), and that the individual’s preferred representational sensory system could explain manifested behavior and characteristics in the managerial and marketing sphere. The central purpose of the research and the significance of choice made about this area of interest is to determine each individual’s preferred representational sensory system (VAK) utilizing the original PRSS-VAK methodology. The methodological research tool was the PRSS-VAK methodology which contains nine statements, which are assessed on a scale from 1 (the least describes me) to 4 (the most describes me). The PRSS-VAK methodology would help to comprehend patterns of an individual’s behavior and allied cognitive or emotional processes. EFA (Exploratory Factor Analysis) with Varimax rotation was used to verify the methodology on a sample of 214 respondents from the Slovak Republic, and CFA (Confirmatory Factor Analysis) was used to validate the structure on a sample of 268 respondents from the Slovak Republic. This research empirically and theoretically confirms that one of the preferred representational sensory systems may be dominant. However, this may change regarding the current situation (stimulus, impulse). The research results could be beneficial as a springboard not only for researchers concerning this issue. It also indicates that quantitative research does not determine exactly to which category (visual, auditory, or kinesthetic) a certain individual belongs. Using the identification of a preferred representational sensory system could help to facilitate both management and marketing communications.

Real-World Data Difficulty Estimation with the Use of Entropy

In the era of the Internet of Things and big data, we are faced with the management of a flood of information. The complexity and amount of data presented to the decision-maker are enormous, and existing methods often fail to derive nonredundant information quickly. Thus, the selection of the most satisfactory set of solutions is often a struggle. This article investigates the possibilities of using the entropy measure as an indicator of data difficulty. To do so, we focus on real-world data covering various fields related to markets (the real estate market and financial markets), sports data, fake news data, and more. The problem is twofold: First, since we deal with unprocessed, inconsistent data, it is necessary to perform additional preprocessing. Therefore, the second step of our research is using the entropy-based measure to capture the nonredundant, noncorrelated core information from the data. Research is conducted using well-known algorithms from the classification domain to investigate the quality of solutions derived based on initial preprocessing and the information indicated by the entropy measure. Eventually, the best 25% (in the sense of entropy measure) attributes are selected to perform the whole classification procedure once again, and the results are compared.

Assessing Social Communication and Collaboration in Autism Spectrum Disorder Using Intelligent Collaborative Virtual Environments

Existing literature regarding social communication outcomes of interventions in autism spectrum disorder (ASD) depends upon human raters, with limited generalizability to real world settings. Technological innovation, particularly virtual reality (VR) and collaborative virtual environments (CVE), could offer a replicable, low cost measurement platform when endowed with intelligent agent technology and peer-based interactions. We developed and piloted a novel collaborative virtual environment and intelligent agent (CRETA) for the assessment of social communication and collaboration within system and peer interactions. The system classified user statements with moderate to high accuracies. We found moderate to high agreement in displayed communication and collaboration skills between human-human and human-agent interactions. CRETA offers a promising avenue for future development of autonomous measurement systems for ASD research.