DRIVERS’ HAZARD AVOIDANCE DURING VEHICLE AUTOMATION: IMPACT OF MENTAL MODELS AND IMPLICATIONS FOR TRAINING

Advanced Driver Assistance Systems (ADAS) are vehicle automation systems that have become more accessible and prevalent in vehicles in recent years. But the introduction of such technologies introduces new human factors challenges. Past literature suggests that users of vehicle automation lack the necessary and appropriate knowledge about their automation system. This may play a negative role in their hazard avoidance abilities when driving with automation features. Improving mental models and knowledge could generally lead to safer interactions with vehicle automation systems, but any effort to develop hazard avoidance skills when driving with vehicle automation is impeded by the lack of literature regarding the subject. Moreover, it is possible hazard avoidance for vehicle automation may actually differ from that for traditional driving. For vehicle automation, system-related changes occurring internally inside one’s vehicle also impact how the system responds and controls the vehicle. Failure to recognize certain critical system changes may have disastrous consequences. Hence, it is imperative that a new framework for hazard avoidance in the new context of vehicle automation, especially for ADAS features, is conceptualized. Initially, the research focused on realizing exactly this by proposing a conceptual framework for hazard avoidance in the context of vehicle automation by making use of past literary sources on hazard avoidance for traditional driving. Next, the relationship between mental models, training, and hazard avoidance was mapped and each new behavioral construct of hazard avoidance focusing on awareness, detection, and responses based on internal events was assigned potential outcome measure. Next, an observational study was conducted with ten experienced users of Adaptive Cruise Control (ACC). Among them, five were assigned to an eye movements group and five others to a verbal responses group. The eye movement observations gave us insights into how experienced users detect and respond to hazards and how these affect their interactions and responses using their ACC systems. The verbal group also provided insights about the participants’ awareness during the drive which featured several edge-case and normal events. These observations imply that hazard avoidance behaviors actually differ in the context of ADAS compared to traditional driving. The findings from the observational study were leveraged when designing and developing a new training program where drivers would receive an immersive and realistic training experience through a Virtual Reality (VR) headset. The main objective of the training program was to improve the user’s mental models about ACC and also equip them with the necessary skills to avoid hazard during edge case events of ACC. Finally, an evaluation study was conducted with 36 novice ACC users on a driving simulator capable of simulating ACC operations. The participants were equally and randomly assigned to one of three group – the VR group that received the newly designed VR training program; the SD group that received training material with state diagram visualization of ACC and other information derived from owner’s manuals; or the BI group that received basic textual information about ACC. The participants’ mental models before and after training were measured using a mental models survey, and the simulator drive was designed to collect valuable data about the participants interactions with ACC and their hazard avoidance behaviors. Findings revealed that although the VR training program had some impact on the participants\u27 mental models and hazard avoidance behaviors, the impact was not statistically significant. However, the VR training did show significantly positive influences on the participants’ internal glance activities that detect and assess system states, during edge case events. This finding is important since one of the modules of the VR training program was carefully curated to improve driver’s glance behavior when encountering edge case events of ACC. The results also establish the relationships between training and mental models although no significant correlations were found between the participants’ mental models and their hazard avoidance behaviors. However, this does fill a major gap in literature about our understanding about hazard avoidance in the context of vehicle automation and ADAS and could be extended for ADAS features other than ACC or even higher levels of automation. The VR training program can be built upon to include more ADAS features as well leading to better training practices in a rapidly developing world where vehicle automation has become a mainstay

The Diagnosticity of Argument Diagrams

Can argument diagrams be used to diagnose and predict argument performance? Argumentation is a complex domain with robust and often contradictory theories about the structure and scope of valid arguments. Argumentation is central to advanced problem solving in many domains and is a core feature of day-to-day discourse. Argumentation is quite literally, all around us, and yet is rarely taught explicitly. Novices often have difficulty parsing and constructing arguments particularly in written and verbal form. Such formats obscure key argumentative moves and often mask the strengths and weaknesses of the argument structure with complicated phrasing or simple sophistry. Argument diagrams have a long history in the philosophy of argument and have been seen increased application as instructional tools. Argument diagrams reify important argument structures, avoid the serial limitations of text, and are amenable to automatic processing. This thesis addresses the question posed above. In it I show that diagrammatic models of argument can be used to predict students' essay grades and that automatically-induced models can be competitive with human grades. In the course of this analysis I survey analytical tools such as Augmented Graph Grammars that can be applied to formalize argument analysis, and detail a novel Augmented Graph Grammar formalism and implementation used in the study. I also introduce novel machine learning algorithms for regression and tolerance reduction. This work makes contributions to research on Education, Intelligent Tutoring Systems, Machine Learning, Educational Datamining, Graph Analysis, and online grading

An influence model of the experience of learning programming

Learning to program is difficult for many students all over the world with programming courses often experiencing high failure and attrition rates. The teaching of programming is still considered a major challenge by educators. At the same time, programming is becoming a key skill required not only of IT graduates but also of students in other disciplines and is becoming more important to a wider range of people. Today’s university students also practice their learning in an extended learning environment that extends well beyond the classroom. There has been considerable research into the teaching of programming in the computing education field, with many studies focussing on content and delivery. More recently, researchers have recognised the need for a greater understanding of how students experience learning to program, from the student’s perspective. This study contributes to this growing body of knowledge by exploring, in depth, the wide range of influences on the student learning experience of programming. A qualitative study was conducted that interviewed 31 Information Systems students about their experiences in learning programming. The interview transcripts were analysed using a Grounded Theory methodology. A new theory of the Influences on the Student Learning Experience of Programming was developed from the analysis, which is more holistic and comprehensive than previous theories. The learning experience of programming involves a complex interaction of a wide range of influences. A major influence is the student’s Perceived Personal Relevance towards programming. Students who perceive that programming is relevant to their future career goals are far more motivated to learn it. Perceived Personal Relevance, together with Learning Trait and Skill Level describe the Learner Nature of the student, which influences their Learning Behaviours. The influences within Learning Behaviours include Core Learning Perspectives (Ownership of learning, Learning Task Intent and Problem solving Behaviours), Patterns of Collaboration and Patterns of Information Use. Patterns of Collaboration describe how students interact with and use their Personal Networks, and include four levels of dependency: One Way Dependent, Two Way Co-Dependent, Collaborative Independent and Solitary Independent. Patterns of Information Use describe the different ways students interact with and use their information sources. The theory includes Programming Learner Profiles, which encapsulate the relationships and influences between Learner Nature and Learning Behaviours. Each profile describes, in essence, the nature and behaviour of different types of students. Seven distinct Programming Learner Profiles were identified in the study: Reluctant Beginner, Willing Beginner, Keen Beginner, Budding Manager, Budding Practitioner, Budding Developer and Advanced Developer. This new theory gives educators a greater insight into what students are thinking and doing when learning to program and potential strategies that can improve learning outcomes