The protection of proprietary information that users print from their information systems is a significant and relevant concern in the field of information security to both researchers and practitioners. Information security researchers have repeatedly indicated that human behaviors and perception are important factors influencing the information security of organizations and have called for more research. The aerospace and defense industry commonly deals with its own proprietary information as well its customers. Further, e-training is a growing practice in this industry, it frequently deals with proprietary information, and has unique information security challenge, thus, serves as additional context for this study. This study focused on the investigation of two constructs, user reading preference and user perceived risk of compromising printed proprietary information, as well as seven user demographics. These constructs reflect human behavior and risk perceptions associated with compromising printed proprietary information and, thus, provide valuable insights applicable into information security. This study developed a Reading Preference and Risk (RPR) Taxonomy, which allows users to be classified according to the aforementioned two constructs under investigation and provides insightful characterizations of information security risks. A survey based on existing literature, the primary constructs, and several demographics was implemented to assess two research questions and seven associated hypotheses. The survey was sent to 1,728 employees of an aerospace and defense organization. The response rate was 18% with 311 usable records. The results of the study showed that employees were dispersed across the RPR Taxonomy with 15.1% identified as potentially problematic to the protection of printed proprietary information. The overall results showed that the population had a reading preference for print materials and a high perceived risk for compromising printed proprietary information, as well as significantly higher print preference for e-training materials when it was necessary to retain the content in memory. Significant differences in the two constructs were also found across several demographics including age, gender, frequency of user exposure to proprietary information, the confidentiality level of the proprietary information a user is regularly exposed to, and previous user experience with the compromise of proprietary information. Recommendations for practice and research are provided. Moreover, several areas for future research are also presented.

As the amount of information captured about users increased over the last decade, interest in personalized user interfaces has surged in the HCI and IS communities. Personalization is an effective means for accommodating for differences between individuals. The fundamental idea behind personalization rests on the notion that if a system can gather useful information about the user, generate a relevant user model and apply it appropriately, it would be possible to adapt the behavior of a system and its interface to the user at the individual level. Personal-ization of a user interface features can enhance usability. With recent technological advances, personalization can be achieved automatically and unobtrusively. A user interface can deploy a NeuroIS technology such as eye-tracking that learns from the user's visual behavior to provide users an experience most unique to them. The advantage of eye-tracking technology is that subjects cannot consciously manipulate their responses since they are not readily subject to manipulation. The objective of this dissertation is to develop a theoretical framework for user personalization during reading comprehension tasks based on two machine learning (ML) models. The proposed ML-based profiling process consists of user's age characterization and user's cognitive load detection, while the user reads text. To this end, detection of cognitive load through eye-movement features was investigated during different cognitive tasks (see Chapters 3, 4 and 6) with different task conditions. Furthermore, in separate studies (see Chapters 5 and 6) the relationship between user's eye-movements and their age population (e.g., younger and older generations) were carried out during a reading comprehension task. A Tobii X300 eye tracking device was used to record the eye movement data for all studies. Eye-movement data was acquired via Tobii eye tracking software, and then preprocessed and analyzed in R for the aforementioned studies. Machine learning techniques were used to build predictive models. The aggregated results of the studies indicate that machine learning accompanied with a NeuroIS tool like eye-tracking, can be used to model user characteristics like age and user mental states like cognitive load, automatically and implicitly with accuracy above chance (range of 70-92%). The results of this dissertation can be used in a more general framework to adaptively modify content to better serve the users mental and age needs. Text simplification and modification techniques might be developed to be used in various scenarios.

Jag har i denna studie haft som syfte att undersöka hur förmågan att skriva för hand ser ut för elever i mellanstadieålder i en svensk grundskola. Mitt syfte har också varit att undersöka om det finns ett samband mellan förmågan att skriva för hand och förmågan att skriva med textkvalitet. För att nå svar på mina frågeställningar genomförde jag en undersökning där 25 elever deltog. Undersökningen innehöll fyra testdelar som vardera var designade för att mäta olika delar i förmågan att skriva för hand och även ämnade att synliggöra förmågan att skriva med textkvalitet. Den forskning som ligger till grund för den här studien visar att det finns ett samband mellan en automatiserad förmåga att skriva för hand, förmåga att skriva med textkvalitet och skolframgång. Orsaken till detta samband förklaras av forskningen i hur olika minnesstrukturer kan användas och avlastas vid automatiserade förmågor jämfört med icke automatiserade förmågor. Resultatet av studien visar att det finns en stor variation i den undersökta elevgruppens förmåga att skriva för hand. Tydliga tendenser pekar även på att det finns ett samband mellan en automatiserad förmåga att skriva för hand och en förmåga att skriva med textkvalitet.

Effective communication is crucial for successful behavior change. However, despite much research in training and development, instructional communication, and public health surrounding communication, it is still unclear what constitutes such effective delivery behaviors, especially for an adult learner population (those over 25 years old). Using cognitive load theory and cognitive-affective theory of learning with media as theoretical frameworks, this dissertation proposes a quality of delivery scale for measuring effective communication across instructional settings with an adult learner audience. Informed by public health, training and organizational communication, as well as adult education and instructional communication, the final valid and reliable QD scale consists of seven communication characteristics that are associated with reduced cognitive load, increased motivation, and increased compliance. Ultimately, this three-phase study consisted of: (a) developing the QD scale, (b) confirming the factor structure, as well as convergent and predictive validity, and (c) testing a theoretical model of QD.

A longstanding folk belief suggests that 'busy' people possess the ability to get more done than others. Busyness, defined as the demands of everyday life, has been shown to generate cognitive load, which has been called 'cognitive busyness.' Although most cognitive theory would deny the possibility that cognitive load may enhance performance, some recent research may support the possibility. Cowan's 1988 information-processing model was used to study how measures of everyday busyness correlated with performance on cognitive tasks. The research question addressed whether any combination of such measures, in combination with working memory, could predict performance on such tasks. 92 participants, paid workers with Amazon Mechanical Turk, engaged in an online process, starting with completion of a validated self-report instrument to measure busyness. They then participated in 2 activities, structured as games and designed to measure working memory and cognitive performance. Multiple regressions, linear and nonlinear, were used to identify significant predictors of performance. Results of the analyses did not reveal any evidence for significant relationships between the variables. Additionally, 'volitional busyness' did not appear to enhance, or even affect, performance on a planning task. Further research exploring the effect of these variables on a working memory-based task may be worthwhile, if only to confirm the present findings. This project might benefit linguists tracking semantic change, showing how a term may adopt an entirely different meaning and suggesting further refinement in identifying such shifts over the years; psychologists exploring cognitive load and its effects; and social psychologists interested in making corrections to popular perceptions of the value of tradition gender-associated tasks.

Modern systems produce a great amount of information and cues from which human operators must take action. On one hand, these complex systems can place a high demand on an operator's cognitive load, potentially overwhelming them and causing poor performance. On the other hand, some systems utilize extensive automation to accommodate their complexity; this can cause an operator to become complacent and inattentive, which again leads to deteriorated performance (Wilson, Russell, 2003a; Wilson, Russell, 2003b). An ideal human-machine interface would be one that optimizes the functional state of the operator, preventing overload while not permitting complacency, thus resulting in improved system performance. An operator's functional state (OFS) is the momentary ability of an operator to meet task demands with their cognitive resources. A high OFS indicates that an operator is vigilant and aware, with ample cognitive resources to achieve satisfactory performance. A low OFS, however, indicates a non-optimal cognitive load, either too much or too little, resulting in sub-par system performance (Wilson, Russell, 1999). With the ability to measure and detect changes in OFS in real-time, a closed-loop system between the operator and machine could optimize OFS through the dynamic allocation of tasks. For instance, if the system detects the operator is in cognitive overload, it can automate certain tasks allowing them to better focus on salient information. Conversely, if the system detects under-vigilance, it can allocate tasks back to the manual control of the operator. In essence, this system operates to "dynamically match task demands to [an] operator's momentary cognitive state", thereby achieving optimal OFS (Wilson, Russell, 2007). This concept is termed adaptive aiding and has been the subject of much research, with recent emphasis on accurately assessing OFS in real-time. OFS is commonly measured indirectly, like using overt performance metrics on tasks; if performance is declining, a low OFS is assumed. Another indirect measure is the subjective estimate of mental workload, where an operator narrates his/her perceived functional state while performing tasks (Wilson, Russell, 2007). Unfortunately, indirect measures of OFS are often infeasible in operational settings; performance metrics are difficult to construct for highly-automated complex systems, and subjective workload estimates are often inaccurate and intrusive (Wilson, Russell, 2007; Prinzel et al., 2000; Smith et al., 2001). OFS can be more directly measured via psychophysiological signals such as electroencephalogram (EEG) and electrooculography (EOG). Current research has demonstrated these signals' ability to respond to changing cognitive load and to measure OFS (Wilson, Fisher, 1991; Wilson, Fisher, 1995; Gevins et al., 1997; Gevins et al., 1998; Byrne, Parasuraman, 1996). Moreover, psychophysiological signals are continuously available and can be obtained in a non-intrusive manner, pre-requisite for their use in operational environments. The objective of this study is to advance schemes which detect change in OFS by monitoring psychophysiological signals in real-time. Reviews on similar methods can be found in, e.g., Wilson and Russell (2003a) and Wilson and Russell (2007). Many of these methods employ pattern recognition to classify mental workload into one of several discrete categories. For instance, given an experiment with easy, medium and hard tasks, and assuming the tasks induce varying degrees of mental workload on a subject, these methods classify which task is being performed for each epoch of psychophysiological data. The most common classifiers are artificial neural networks (ANN) and multivariate statistical techniques such as stepwise discriminant analysis (SWDA). ANNs have proved especially effective at classifying OFS as they account for the non-linear and higher order relationships often present in EEG/EOG data; they routinely achieve classification accuracy greater than 80%. However, the discrete output of these classification schemes is not conducive to real-time change detection. They accurately classify OFS, but they do not indicate when OFS has changed; the change points remain ambiguous and left to subjective interpretation. Thus, the present study introduces several online algorithms which objectively determine change in OFS via real-time psychophysiological signals. The following chapters describe the dataset evaluated, discuss the statistical properties of psychophysiological signals, and detail various algorithms which utilize these signals to detect real-time changes in OFS. The results of the algorithms are presented along with a discussion. Finally, the study is concluded with a comparison of each method and recommendations for future application.

Researchers and educators have always strived for creating appropriate instructional tools and resources that help students to acquire knowledge. Animated pedagogical agents (APAs) embedded within multimedia learning settings are one of the emerging technologies that provide a powerful and supportive learning environment. According to previous studies, APAs can effectively promote learning and support social interaction with learners (Johnson & Lester, 2016; Lane, 2016). However, APAs also may cause cognitive load without providing motivational benefits in some cases and distract learners during the learning process. In other words, the results of previous studies on APAs do not provide enough evidence to argue that APA may be able to decrease cognitive load, promote motivational effects, or facilitate meaningful learning. The lack of enough evidence in the research findings seems to be variable depending upon the APA’s features, the learners, and the difficulty of the learning materials (Schroeder & Adesope, 2014). By focusing on these factors, this study provided new considerations related to embedding an APA’s role that facilitates “Word Parts” for adult students who speak English as a Second Language (ESL) with concentrating on their cognition, motivation, and vocabulary acquisition. Presenting two APA’s roles (expert model and peer model) differently influenced ESL learners’ motivation, specifically their satisfaction feelings. In addition, ESL learners’ prior knowledge affected their intrinsic and extraneous cognitive load, motivation, and vocabulary acquisition. The two different APA’s roles and their effects on ESL learners’ perceptions and learning outcomes serve as a media comparison research. Further, examining APA as a model to teach ESL students vocabulary acquisition skills serves a Computer-Assisted Language Learning (CALL) research.

Master of Applied Science / The aim of this investigation was to investigate the effects of computer-based visual feedback in the teaching of singing. Pitch accuracy, a readily-measured parameter of the singing voice, was used in this study to gauge changes in singing for groups with and without visual feedback. The study investigated whether the style of feedback affects the amount of learning achieved, and whether the provision of concurrent visual feedback hampers the simultaneous performance of the singing task. The investigation used a baseline–intervention–post-test between-groups design. Participants of all skill levels were randomly assigned to a control group or one of two experimental groups – with all participants given one hour of singing training. At intervention, the two experimental groups were offered one of two different displays of real-time visual feedback on their vocal pitch accuracy, while control participants had a non-interactive display. All sessions were recorded, and the vocal exercise patterns performed at baseline, intervention and post-test phases were acoustically analysed for pitch accuracy. Questionnaires assessed both general health and the amount of singing and music training of all participants; people in the two experimental groups were also given a further questionnaire about the visual feedback. The results indicate that visual feedback improves pitch accuracy in singing. Cognitive load related to the decoding of visual information was a factor at intervention. At post-test, the two groups who had used real-time visual feedback demonstrated marked improvement on their initial pitch accuracy. There was no significant difference between the results of participants from the two experimental groups, although the participants with some background in singing training showed greater improvement using a simpler visual feedback design. The findings suggest that a hybrid approach integrating standard singing teaching practices with real-time visual feedback of aspects of the singing voice may improve learning.

Cognitive limitations such as those described in Miller's (1956) work on channel capacity and Cowen's (2001) on short-term memory are factors in determining user cognitive load and in turn task performance. Inappropriate user cognitive load can reduce user efficiency in goal realization. For instance, if the user's attentional capacity is not appropriately applied to the task, distractor processing can tend to appropriate capacity from it. Conversely, if a task drives users beyond their short-term memory envelope, information loss may be realized in its translation to long-term memory and subsequent retrieval for task base processing. To manage user cognitive capacity in the task of text search the interface should allow users to draw on their powerful and innate pattern recognition abilities. This harmonizes with Johnson-Laird's (1983) proposal that propositional representation is tied to mental models. Combined with the theory that knowledge is highly organized when stored in memory an appropriate approach for cognitive load optimization would be to graphically present single documents, or clusters thereof, with an appropriate number and type of descriptors. These descriptors are commonly words and/or phrases. Information theory research suggests that words have different levels of importance in document topic differentiation. Although key word identification is well researched, there is a lack of basic research into human preference regarding query formation and the heuristics users employ in search. This lack extends to features as elementary as the number of words preferred to describe and/or search for a document. Contrastive understanding these preferences will help balance processing overheads of tasks like clustering against user cognitive load to realize a more efficient document retrieval process. Common approaches such as search engine log analysis cannot provide this degree of understanding and do not allow clear identification of the intended set of target documents. This research endeavours to improve the manner in which text search returns are presented so that user performance under real world situations is enhanced. To this end we explore both how to appropriately present search information and results graphically to facilitate optimal cognitive and perceptual load/utilization, as well as how people use textual information in describing documents or constructing queries.

<p>This thesis undertakes two main tasks; to explore, within the authentic educational context, variables that influence the quality and outcomes of the knowledge-constructing activity during the simulation exercise; and to find appropriate instruments and methods to measure these variables, processes and outcomes.</p><p>Closed-ended tasks that provided a high degree of structure, i.e., supported students’ regulation of learning during the simulation exercise, were more beneficial for perceived improvement of declarative knowledge and students’ motivation than open-ended tasks. Open-ended tasks did however lead to substantial shifts in students’ cognitive focus in subsequent laboratory exercises and improvement of students’ level of chemistry reasoning in interviews.</p><p>Student attitudes toward learning proved important in the first paper where significantly higher ability to apply chemistry knowledge in interviews could be found for students with relativistic attitudes compared to those with more dualistic perspectives on learning. In the subsequent papers, the effects of attitudes were not as clear, possibly due to too small differences in the learning situations that were compared.</p><p>Quality of the learning discourse during simulation was measured with three qualitatively different methods, focusing on partly different aspects. The different methods gave very similar results regarding the relative quality of the discourses. Thus, “quality” as such seems to be an underlying feature that permeates many aspects of the discourse and consequently could be targeted in different ways, e.g., focusing on quantitative as well as qualitative aspects. The analyses revealed several components of quality; co-operative activity, correctness and complexity of chemistry reasoning, discussion length and intensity, ability to realize cognitive conflict, and reference to theory while reasoning.</p><p>Doing the simulation exercise in a distributed learning setting (written e-communication), supported discussions with higher accuracy and complexity of chemistry reasoning and frequent references to theory while the face-to-face situation allowed for longer and more intense discussions and a higher degree of co-operative activity. Not very surprisingly, high-quality discussions were characterized by high scores in all these components. There were indications that relatively good pre-knowledge might be required to benefit fully from face-to-face discussions.</p><p>The validity of instruments and methods, used to measure flow experiences, attitudes (i.e., epistemological beliefs), knowledge accessibility (intuitive knowledge) and discourse quality are discussed thoroughly. Special interest has been devoted to whether qualitative data should be quantified or not, providing arguments in favor of quantitative methods for analyzing and reporting qualitative data.</p>