Enhancing Analysts’ Mental Models for Improving Requirements Elicitation: A Two-stage Theoretical Framework and Empirical Results

Research has extensively documented the importance of accurate system requirements in avoiding project delays, cost overruns, and system malfunctions. Requirement elicitation (RE) is a critical step in determining system requirements. While much research on RE has emerged, a deeper understanding of three aspects could help significantly improve RE: 1) insights about the role and impacts of support tools in the RE process, 2) the impact of using support tools in multiple stages of the RE process, and 3) a clear focus on the multiplicity of perspectives in assessing RE outcomes. To understand how using support tools could improve RE, we rely on the theoretical lens of mental models (MM) to develop a dynamic conceptual model and argue that analysts form mental models (MMs) of the system during RE and these MMs impact their outcome performance. We posit that one can enhance analysts’ MMs by using a knowledge-based repository (KBR) of components and services embodying domain knowledge specific to the target application during two key stages of RE, which results in improved RE outcomes. We measured the RE outcomes from user and analyst perspectives. The knowledge-based component repository we used in this research (which we developed in collaboration with a multi-national company) focused on insurance claim processing. The repository served as the support tool in RE in a multi-period lab experiment with multiple teams of analysts. The results supported the conceptualized model and showed the significant impacts of such tools in supporting analysts and their performance outcomes at two stages of RE. This work makes multiple contributions: it offers a theoretical framework for understanding and enhancing the RE process, develops measures for analysts’ mental models and RE performance outcomes, and shows the process by which one can improve analysts’ RE performance through access to a KBR of components at two key stages of the RE process

Increasing appropriate social communication and pragmatic language skills in young adults with autism spectrum disorders

This report seeks to explore intervention methodologies that can be adapted for use with young transitional adults with Autism Spectrum Disorders (ASD) to increase appropriate social communication. No interventions have been developed explicitly for use in speech-language therapy with young adults with ASD. As a result, existing interventions have to be adapted to use with this population. The interventions that were adapted were Behavioral Interventions, Developmental Social Pragmatic Interventions, and Enhanced Milieu Teaching. The adaptations of the reviewed interventions were illustrated by creating an instructional video to highlight how to use the interventions and their adaptations with an adult with ASD. The author and participant consider the video development and final product to be a success; however, until peer-reviewed efficacy studies that explore the effects of these intervention adaptations with young-adults with ASD are conducted, the author cannot assert that the intervention adaptations discussed will be effective therapy strategies for young adults with ASD.Communication Sciences and Disorder

Robust Modeling of Epistemic Mental States

This work identifies and advances some research challenges in the analysis of facial features and their temporal dynamics with epistemic mental states in dyadic conversations. Epistemic states are: Agreement, Concentration, Thoughtful, Certain, and Interest. In this paper, we perform a number of statistical analyses and simulations to identify the relationship between facial features and epistemic states. Non-linear relations are found to be more prevalent, while temporal features derived from original facial features have demonstrated a strong correlation with intensity changes. Then, we propose a novel prediction framework that takes facial features and their nonlinear relation scores as input and predict different epistemic states in videos. The prediction of epistemic states is boosted when the classification of emotion changing regions such as rising, falling, or steady-state are incorporated with the temporal features. The proposed predictive models can predict the epistemic states with significantly improved accuracy: correlation coefficient (CoERR) for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special Issue: Socio-Affective Technologie

Semantic-driven knowledge-enabled cognitive decision support system

University of Technology, Sydney. Faculty of Engineering and Information Technology.The importance of knowledge and cognition in business intelligence and decision support systems (DSS) is indisputable. However two major issues, a) biases in cognition, and b) knowledge integration overhead in knowledge warehousing, hinder their optimum utility in such systems. We address the issue of biases by proposing semantic de-biased associations (SDA) model, which is an improvement over the conventional causal map representation of mental models. SDA model incorporates semantics and contextual information to implement automated de-biasing by employing de-biasing techniques and algorithm into the inherent process of mental model elicitation, storage and retrieval. An elicitation process customised for SDA-based representation was also proposed namely SDA articulation and elicitation cycle. SDA model automates the process of mental model validation and integration, so as to prevent any espoused theories to be stored in the system. It also provides faster access to relevant knowledge, while creating a knowledge cycle between user and the system, which provides learning and knowledge growth opportunities to the system users, promoting organizational learning. The issue of knowledge integration overhead is dealt with by proposing a unified, standard storage structure for knowledge warehousing in subject-oriented semantic knowledge warehouse (SSKW). The unified storage structure is achieved through categorising knowledge on syntactic level, and creating universal templates of these categories. In addition, the rules of how they can be connected together are outlined. The categories of knowledge, formalised, are object, process, and event. The connections between them are implemented through semantic relationships. The SSKW provides a domain-independent knowledge warehousing architecture to store knowledge in a subject-oriented, semantic, integrated, systematic and meaningful manner. It incorporates object-oriented, semantic, and human-centric approaches to facilitate an intuitive and efficient communication. It prevents loss of knowledge, improves precision of output, and ensures efficient delivery of knowledge when required. The SDA model and SSKW are integrated together in this research to form a human-centric DSS, semantic-driven knowledge-enabled cognitive decision support system (SCDSS). SCDSS accumulates knowledge of many decision makers over time, thus if a decision maker leaves the organisation, his/her knowledge is retained through this system. Moreover, it automates the dissemination of knowledge across the organisation. Two evaluations were conducted to measure the performance of SCDSS against selected criteria. The results of the evaluations show that SCDSS successfully mitigates availability, framing, contextual and group biases, and generates new knowledge during decision making process. The results also demonstrate the effectiveness of SCDSS in knowledge sharing and enhancement, efficiency in producing output; and the relevance of knowledge in the output. The system can be accessed at http://tasneememon.com/SCDSS/index.php

From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support

OBJECTIVES: 1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; 2) To exploit expert knowledge in the BN development since further data acquisition is usually not possible; 3) To ensure the BN model can be used for interventional analysis; 4) To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available. METHOD: The method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data. While most of the components of the method are based on established work, its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life-cycle of BN model development. The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P. RESULTS: When employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0.708 and 0.797) against the state-of-the-art (AUC scores ranging from 0.527 to 0.705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0.78) against the state-of-the-art (AUC scores ranging from 0.665 to 0.717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence. CONCLUSIONS: This development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of expert knowledge for decision support via intervention. The novelty extends to challenging the decision scientists to reason about building models based on what information is really required for inference, rather than based on what data is available and hence, forces decision scientists to use available data in a much smarter way

Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: A comparative study

Deficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the underlying true emotional state of a person. This study applied machine-learning algorithms to categorize EEG emotional states in PD patients that would classify six basic emotions (happiness and sadness, fear, anger, surprise and disgust) in comparison with healthy controls (HC). Emotional EEG data were recorded from 20 PD patients and 20 healthy age-, education level- and sex-matched controls using multimodal (audio-visual) stimuli. The use of nonlinear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to classify the emotional states. In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Analysis of variance (ANOVA) showed that power spectrum and HOS based features were statistically significant among the six emotional states (p < 0.0001). Classification results shows that using the selected HOS based features instead of power spectrum based features provided comparatively better accuracy for all the six classes with an overall accuracy of 70.10% ± 2.83% and 77.29% ± 1.73% for PD patients and HC in beta (13-30 Hz) band using SVM classifier. Besides, PD patients achieved less accuracy in the processing of negative emotions (sadness, fear, anger and disgust) than in processing of positive emotions (happiness, surprise) compared with HC. These results demonstrate the effectiveness of applying machine learning techniques to the classification of emotional states in PD patients in a user independent manner using EEG signals. The accuracy of the system can be improved by investigating the other HOS based features. This study might lead to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders

Planning and Team Shared Mental Models as Predictors of Team Collaborative Processes

This study evaluates the role of team planning and the similarity of team shared mental models (TSMMs) as predictors of two types of collaborative behaviors that are known to contribute to team performance. A computer-based Networked Fire Chief (NFC) simulation task was used as a testing environment for emergent and dynamic situations. The relationships among team planning, similarity of task-focused team shared mental models (TASKTSMMs), similarity of team-focused team shared mental models (TEAMTSMMs), team backup behaviors, and implicit coordination were tested. This study provides evidence for the mediation effect of similarity of TASKTSMMs between team planning and team backup behaviors, and the mediation effect of team backup behaviors between similarity of TASKTSMMs and team performance. The results suggest that better team planning is more likely to encourage more backup behaviors and improved performance through teams having more similar task-focused mental models. Both the theoretical and practical implications were discussed and the limitations and future research were also addressed in the study

Expert knowledge elicitation to improve mental and formal models

Includes bibliographical references (p. 24-25).Supported by the Python organization, the Organizational Learning Center and the System Dynamics Group at the MIT Sloan School of Management.David N. Ford and John D. Sterman