On the Mental Workload Assessment of Uplift Mapping Representations in Linked Data

Self-reporting procedures have been largely employed in literature to measure the mental workload experienced by users when executing a specific task. This research proposes the adoption of these mental workload assessment techniques to the task of creating uplift mappings in Linked Data. A user study has been performed to compare the mental workload of “manually” creating such mappings, using a formal mapping language and a text editor, to the use of a visual representation, based on the block metaphor, that generate these mappings. Two subjective mental workload instruments, namely the NASA Task Load Index and the Workload Profile, were applied in this study. Preliminary results show the reliability of these instruments in measuring the perceived mental workload for the task of creating uplift mappings. Results also indicate that participants using the visual representation achieved smaller and more consistent scores of mental workload

A Novel Parabolic Model of Instructional Efficiency Grounded on Ideal Mental Workload and Performance

Instructional efficiency within education is a measurable concept and models have been proposed to assess it. The main assumption behind these models is that efficiency is the capacity to achieve established goals at the minimal expense of resources. This article challenges this assumption by contributing to the body of Knowledge with a novel model that is grounded on ideal mental workload and performance, namely the parabolic model of instructional efficiency. A comparative empirical investigation has been constructed to demonstrate the potential of this model for instructional design evaluation. Evidence demonstrated that this model achieved a good concurrent validity with the well-known likelihood model of instructional efficiency, treated as baseline, but a better discriminant validity for the evaluation of the training and learning phases. Additionally, the inferences produced by this novel model have led to a superior information gain when compared to the baseline

Noise Reduction of EEG Signals Using Autoencoders Built Upon GRU based RNN Layers

Understanding the cognitive and functional behaviour of the brain by its electrical activity is an important area of research. Electroencephalography (EEG) is a method that measures and record electrical activities of the brain from the scalp. It has been used for pathology analysis, emotion recognition, clinical and cognitive research, diagnosing various neurological and psychiatric disorders and for other applications. Since the EEG signals are sensitive to activities other than the brain ones, such as eye blinking, eye movement, head movement, etc., it is not possible to record EEG signals without any noise. Thus, it is very important to use an efficient noise reduction technique to get more accurate recordings. Numerous traditional techniques such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), wavelet transformations and machine learning techniques were proposed for reducing the noise in EEG signals. The aim of this paper is to investigate the effectiveness of stacked autoencoders built upon Gated Recurrent Unit (GRU) based Recurrent Neural Network (RNN) layers (GRU-AE) against PCA. To achieve this, Harrell-Davis decile values for the reconstructed signals’ signal-to- noise ratio distributions were compared and it was found that the GRU-AE outperformed PCA for noise reduction of EEG signals

The comparative Situation Awareness performance of older (to younger) drivers

The overall aim of this thesis is to corroborate whether the Situation Awareness (SA) of older drivers is deficient to that of younger driving groups, due to the onset of age-related cognitive decrements. This is important to ascertain due to a presumed linkage between the concept and accident causation. In addition, the research undertaken to date to investigate this linkage has exclusively utilised rather artificial driving simulators and simulations. Thus there is a need for data from more ecologically valid methods. The research studies reported here have sought to preference on-road assessments (of different complexity), and to capture what information was selectively perceived, comprehended and reacted to; rather than, as in previous work, what was recalled. To achieve this, a Think aloud methodology was chosen to produce narratives of a driver s thoughts. This method was advantageously unobtrusiveness, but also flexible - it could additionally be used to compare an individual's SA to a driving performance measure, Hazard Perception. The driving-based studies undertaken found that for a relatively non-taxing route, an older driver group could produce cohesive awareness in parity with a younger driver group. However, the concepts from which that awareness was based upon drew more on general, direction based, concepts, in contrast to the younger group s focus on more specific, action based, concepts, and rearward and safety-related cues. For a more cognitively taxing route, the younger group produced significantly higher (p<0.024) individual SA-related scores than their older counterparts. But the concepts/cues both groups relied upon remained similar - particularly in regards to the ratio of those indicative of a rearward and/or a safety-related focus. In a video-based study, however, and in contrast, the older driver group s SA scores improved sufficient to outperform a younger group, but, despite this, not for video-based scores indicative of Hazard Perception (HP). In this latter regard, age-related decrements appeared to be more influential, as the older group felt they were under time pressure during a HP test. However, the difficulty this presented appeared to advantageously bring more attention and effort to the task, which were argued as important factors for the uplift in their SA scoring. The thesis also showed that older groups judgement of the actual complexity of a driving task could potentially be deficient to that of younger driver groups. This could cause problems as incorrect perceptions could deflate the relevance and cohesiveness of information being processing. In contrast, the perceived complexity of a task could bring a rise or fall in SA score for both groups. Such results raised questions as to the impact of cognitive decrements, relative to task difficulty and related effort whilst driving. It also provided evidence that Situation Awareness, rather than being uniformly good or bad, could, like any other psychological construct, be prone to change. These aspects were drawn together in a proposed model of driving SA

A Comparison of Instructional Efficiency Models in Third Level Education

This study investigates the validity and sensitivity of a novel model of instructional efficiency: the parabolic model. The novel model is compared against state-of-the-art models present in instructional design today; Likelihood model, Deviational model and Multidimensional model. This models is based on the assumption that optimal mental workload and high performance leads to high efficiency, while other models assume that low mental workload and high performance leads to high efficiency. The investigation makes use of two instructional design conditions: a direct instructions approach to learning and its extension with a collaborative activity. A control group received the former instructional design while an experimental group received the latter design. A performance score was extracted for evaluation. The models of efficiency compared were based upon both a unidimensional and a multidimensional measure of mental workload, which were acquired through self-reporting from the participants. These mental load measures in conjunction with the performance score contribute to the calculation of efficiency scores for each model. The aim of this study is to determine whether the novel model is able to better differentiate between the control and experimental groups based on the resulting efficiency when compared to the other models. The models were analysed and compared using various statistical tests and techniques. Empirical evidence partially supports the proposed hypothesis that parabolic model demonstrates validity, however lacks sufficient statistical evidence to suggest that the model has better sensitivity and its capacity to differentiate between the two groups

Evaluating the Impact of Defeasible Argumentation as a Modelling Technique for Reasoning under Uncertainty

Limited work exists for the comparison across distinct knowledge-based approaches in Artificial Intelligence (AI) for non-monotonic reasoning, and in particular for the examination of their inferential and explanatory capacity. Non-monotonicity, or defeasibility, allows the retraction of a conclusion in the light of new information. It is a similar pattern to human reasoning, which draws conclusions in the absence of information, but allows them to be corrected once new pieces of evidence arise. Thus, this thesis focuses on a comparison of three approaches in AI for implementation of non-monotonic reasoning models of inference, namely: expert systems, fuzzy reasoning and defeasible argumentation. Three applications from the fields of decision-making in healthcare and knowledge representation and reasoning were selected from real-world contexts for evaluation: human mental workload modelling, computational trust modelling, and mortality occurrence modelling with biomarkers. The link between these applications comes from their presumptively non-monotonic nature. They present incomplete, ambiguous and retractable pieces of evidence. Hence, reasoning applied to them is likely suitable for being modelled by non-monotonic reasoning systems. An experiment was performed by exploiting six deductive knowledge bases produced with the aid of domain experts. These were coded into models built upon the selected reasoning approaches and were subsequently elicited with real-world data. The numerical inferences produced by these models were analysed according to common metrics of evaluation for each field of application. For the examination of explanatory capacity, properties such as understandability, extensibility, and post-hoc interpretability were meticulously described and qualitatively compared. Findings suggest that the variance of the inferences produced by expert systems and fuzzy reasoning models was higher, highlighting poor stability. In contrast, the variance of argument-based models was lower, showing a superior stability of its inferences across different system configurations. In addition, when compared in a context with large amounts of conflicting information, defeasible argumentation exhibited a stronger potential for conflict resolution, while presenting robust inferences. An in-depth discussion of the explanatory capacity showed how defeasible argumentation can lead to the construction of non-monotonic models with appealing properties of explainability, compared to those built with expert systems and fuzzy reasoning. The originality of this research lies in the quantification of the impact of defeasible argumentation. It illustrates the construction of an extensive number of non-monotonic reasoning models through a modular design. In addition, it exemplifies how these models can be exploited for performing non-monotonic reasoning and producing quantitative inferences in real-world applications. It contributes to the field of non-monotonic reasoning by situating defeasible argumentation among similar approaches through a novel empirical comparison

Notions of explainability and evaluation approaches for explainable artificial intelligence

Explainable Artificial Intelligence (XAI) has experienced a significant growth over the last few years. This is due to the widespread application of machine learning, particularly deep learning, that has led to the development of highly accurate models that lack explainability and interpretability. A plethora of methods to tackle this problem have been proposed, developed and tested, coupled with several studies attempting to define the concept of explainability and its evaluation. This systematic review contributes to the body of knowledge by clustering all the scientific studies via a hierarchical system that classifies theories and notions related to the concept of explainability and the evaluation approaches for XAI methods. The structure of this hierarchy builds on top of an exhaustive analysis of existing taxonomies and peer-reviewed scientific material. Findings suggest that scholars have identified numerous notions and requirements that an explanation should meet in order to be easily understandable by end-users and to provide actionable information that can inform decision making. They have also suggested various approaches to assess to what degree machine-generated explanations meet these demands. Overall, these approaches can be clustered into human-centred evaluations and evaluations with more objective metrics. However, despite the vast body of knowledge developed around the concept of explainability, there is not a general consensus among scholars on how an explanation should be defined, and how its validity and reliability assessed. Eventually, this review concludes by critically discussing these gaps and limitations, and it defines future research directions with explainability as the starting component of any artificial intelligent system

Culturally-Responsive Canadian Postsecondary Performance Measurement

Student success has multiple meanings; however, the postpositivist bias prevalent in Canadian postsecondary education restricts how student success is defined and measured. When we standardize measures of student success we assume that the student experience is homogeneous and risk implementing policies and programs based on insufficient information. Unless new evaluation approaches are adopted, it is unlikely postsecondary institutions will generate the knowledge and wisdom needed to serve their regional, national, and international learners and communities. Postsecondary education leaders must be cognizant of the legacy of colonialism and consider cultural congruency between performance measurement systems and local context. This organizational improvement plan proposes a theory of action model for culturally-responsive postsecondary performance measurement that leverages shared governance through participatory, emergent, and appreciative processes and qualitative evaluation methodologies. Perception and socially constructed norms play a pivotal role in addressing the postsecondary education sector’s quantitative bias; therefore, an interpretivist lens is used to critically examine the cultural appropriateness of quality assurance and measurement processes at a Canadian university. Culturally-responsive performance measurement requires consideration of diverse worldviews and methodologies. Qualitative evaluation can amplify the lived experiences of students and inform complex policy issues through examination of phenomena and local variability. The next generation of quality assurance requires inclusive decision-making structures to generate collective wisdom and cultivate an ethic of community by engaging community members, faculty, staff, and students as change agents

Proceedings, MSVSCC 2017

Proceedings of the 11th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 20, 2017 at VMASC in Suffolk, Virginia. 211 pp

Journal of the National Collegiate Honors Council, Vol. 23, No. 1, Spring/Summer 2022

Forum Essays on “The Value of Honors to its Graduates”: Authors: Paul Ewing, University of Toledo; Andy Walker, University of Tennessee at Chattanooga; Laura Barrett, LIU Brooklyn; John Major, Ohio State University; Teri Grieb, Columbia College, South Carolina; James A. Keller, University of Delaware; LLeweLLyn Cooper, University of Alabama at Birmingham; Ayesha Ahmed, Northeastern Illinois University; Mary Beth Messner, Youngstown State University; Eric W. Miller, West Virginia University; Sara McCane-Bowling, Eastern Kentucky University; Michelle Panuccio, Youngstown State University; Lia M. Shore, Georgia Perimeter College, Dunwoody; Jennifer N. Dulin, Texas A&M University; Pepper Hayes, Westminster College; Merry Benner Chiu, Adelphi University; Kathryn M. MacDonald, College of New Rochelle; Corey D. Clawson, Utah State University; Jamie Beason, University of North Carolina at Charlotte; Joshua and Brandi Mulanax, Rogers State University; Taylor C. Bybee, Utah State University; Mark Donovan, California State Polytechnic University, Pomona; Colin Christensen, Emory & Henry College; Heather Ness-Maddox, Middle Georgia State University; Claire Guthrie Stasiewicz, University of New Mexico; Seth Blanton, Rogers State University; Ashley Gerstle (née Offenback), Penn State University; Mary Anne Matos, Johnson County Community College, Overland Park, KS; Eli Pemberton, Monroe College; Jonna Nunez, Community College of Baltimore County; Tambria Schroeder, SUNY Brockport; Christopher Kotschevar and Nicholas Arens, South Dakota State University; Sean Collier, Emory & Henry College; Grace Anne Cunningham, Texas A&M University; Emma Labovitz, Appalachian State University; Chloe Salome Margulis, LIU Post; Autumn Barszczowski, Point Park University; Angeline Best, University of Iowa; Samantha Bronow, Oklahoma City University; Emily McAndrew, University of Tennessee at Chattanooga; Joseph Gazing Wolf, California State Polytechnic University, Pomona; Samantha Koprowski, William Paterson University; Quimby Wechter, University of Hartford; Daphne Watson, Northeastern Illinois University. Portz-Prize-Winning Essay, 2021: Refusing Erasure: Nugent, Fire!!, and the Legacies of Queer Harlem • Samantha King-Shaw Research Essays “Best of Both Worlds”: Alumni Perspectives on Honors and the Liberal Arts • Angela King Taylor, Kelsey Daniels, and Molly Knowlton Dutch Honors Alumni Looking Back on the Impact of Honors on their Personal and Professional Development • Arie Kool, Elanor Kamans, and Marca V. C. Wolfensberger Perfectionism and Honors Students: Cautious Good News • Jennifer S. Feenstr