Advance Personalized Learning is one of the 14 grand challenges of engineering as identified by the National Academy of Engineering. One possible approach for this advancement is to deploy systems that allow an investigator to understand the differences in the learning process of individuals. In this context, cyberlearning systems that use networked computing and communication technology to reach a large number of learners offer the affordance to uniquely identify learners and track their learning process in real-time. Motivated by this idea, this doctoral research aims to investigate personalized learning and engagement within a cyberlearning system, called the Online Watershed Learning System (OWLS). This cyberlearning system utilizes learning resources generated by a real-time high-frequency environmental monitoring system, called the Learning Enhanced Watershed Assessment System (LEWAS). The goals include advancing the OWLS with a user tracking system and data availability and visualization features and investigating personalized learning and engagement within the OWLS. A user-tracking system is developed utilizing a Node.js-based Express framework and deployed in the LEWAS server, which identifies individual users across devices such as laptops, tablets, and desktops, and detects their interaction within the OWLS, and stores the interaction data in a PostgreSQL database. HTML, CSS, and JavaScript technologies are used for the client-side development. Informed by the situative theory of learning and engagement theory, an investigation was carried out with 52 students from a junior-level civil engineering class. They completed an OWLS-based in-class task focused on concepts related to the environmental monitoring. Pre and post-surveys and the user-tracking system were utilized to collect data on individual student's perceived and conceptual learning, perceived and behavioral engagement, and perception towards the learning value of the OWLS. Results provide several insights into individual student's learning and engagement with the OWLS. For example, students gained knowledge using the OWLS, and their learning varied with the design of the in-class task, which, however, did not impact their engagement. Further, students' learning (scores on in-class task) had a significant negative relationship with their behavioral engagement (frequency of resource utilization of the OWLS). Additionally, temporal navigational strategies of 52 students were identified on an individual basis. Finally, variations in learning and engagement were analyzed in terms of factors such as gender and background knowledge. The study has implications for designing effective cyberlearning systems and learning activities that can utilize cyberlearning systems for leveraging technology-enhanced teaching and learning. / Ph. D. / Individuals differ in their approaches to learning. For the success of diverse group of learners, the National Academy of Engineering has identified “Advance Personalized Learning” as one of the 14 grand challenges. One possible approach for this advancement is to utilize online learning technologies, such as cyberlearning systems that provide the affordance to uniquely identify each learner and track his/her learning progress allowing an investigator to understand the differences in the learning process of individuals. Motivated by this idea, an interactive cyberlearning system, called the Online Watershed Learning System (OWLS) has been utilized in this study. It contains learning resources generated by a real-time high-frequency environmental monitoring system, called the Learning Enhanced Watershed Assessment System (LEWAS). The goals of the study include: 1) advancing the OWLS with a user tracking system and data availability and visualization features and 2) investigating personalized learning and engagement within the OWLS. For goal 1, cutting-edge technologies were utilized so that OWLS with its user-tracking system can be accessible by large number of users using modern web browsers on devices, such as laptops, tablets and cell phones. For goal 2, classroom implementation was carried out with 52 junior-level civil engineering students, who completed an OWLS-based environmental monitoring task within the class time. Results provide several insights into variation of individual student’s learning and engagement with the OWLS. For example, students gained knowledge using the OWLS, and their learning varied with the design of the in-class task, which, however, did not impact their engagement. Additionally, temporal navigational strategies of 52 students were identified on an individual basis. Variations in learning and engagement were also analyzed in terms of factors such as gender and background knowledge. The study has implications for designing effective cyberlearning systems and learning activities that can utilize cyberlearning system for leveraging technology-enhanced teaching and learning.

The efficient designs of Wireless Sensor Network protocols and intelligent Machine Learning algorithms, together have led to the advancements of various systems and applications for Smart Environments. By definition, Smart Environments are the typical physical worlds used in human daily life, those are seamlessly embedded with smart tiny devices equipped with sensors, actuators and computational elements. Since human user is a key component in Smart Environments, human motion activity patterns have key importance in building sensor network systems and applications for Smart Environments. Motivated by this, in this thesis my work is focused on human motion activity-aware sensor networks for Smart Environments. The main contributions of this thesis are in two important aspects: (i) Designing event activity context-aware sensor networks for efficient performance optimization as well as resource usage; and (ii) Using binary motion sensing sensor networks' collective data for device-free real-time tracking of multiple users. Firstly, I describe the design of our proposed event activity context-aware sensor network protocols and system design for Smart Environments. The main motivation behind this work is as follows. A sensor network, unlike a traditional communication network, provides high degree of visibility into the environmental physical processes. Therefore its operation is driven by the activities in the environment. In long-term operations, these activities usually show certain patterns which can be learned and effectively utilized to optimize network design. In this thesis I have designed several novel protocols: (i) ActSee for activity-aware radio duty-cycling, (ii) EAR for activity-aware and energy balanced routing, and (iii) ActiSen complete working system with protocol suites for activity-aware sensing/ duty-cycling/ routing. Secondly, I have proposed and designed FindingHuMo (Finding Human Motion), a Machine Learning based real-time user tracking algorithm for Smart Environments using Sensor Networks. This work has been motivated by increasing adoption of sensor network enabled Ubiquitous Computing in key Smart Environment applications, like Smart Healthcare. Our proposed FindingHuMo protocol and system can perform device-free tracking of multiple (unknown and variable number of) users in the hallway environments, just from non-invasive and anonymous binary motion sensor data.

Mobile devices have become a massively prevalent part of everyday life, as their capabilities and functionality have expanded into new domains. One form factor that has attracted recent renewed interest is the smartwatch.  This paper looks at how devices can be used to track the time, and in particular how we can invent new visualisations for timekeeping. It draws on different psychological theories of time to sketch out six new time visualisations, drawing on both old and new timekeeping devices as inspiration. These visualisations address linear time visualisations and cyclical time visualisations. The designs are contrasted with each other, with a final design selected and prototyped on the Apple Watch. The prototype is briefly evaluated through a 48 hour user test with one user. The design is then reiterated upon based on the feedback from this user test. / Mobila enheter har blivit en allt större del av våra vardagliga liv i samma takt som deras kapacitet och funktionalitet har ökat och spridit sig till nya områden. Ett av dessa områden som nyligen fått ett nyfunnet intresse är smarta klockor. Den här uppsatsen tittar på hur mobila enheter och framför allt smarta klockor kan användas för att hålla reda på tiden. Den undersöker framför allt på hur vi kan ta fram nya sätt att visualisera tid. Arbetet bygger på olika psykologiska teorier om tid för att skissa sex stycken olika tidsvisualiseringar, de olika visualiseringarna använder också historiska tidmätningsinstrument som inspiration. Dessa visualiseringar bygger också på ett framtaget koncept om att tid kan ses som konstant eller cyklisk. Designförslagen jämförs sedan med varandra och en slutgiltig design väljs ut. En prototyp för Apple Watch skapas, baserat på den slutgiltiga designen. Prototypen utvärderas genom att en användare bär klockan under 48 timmar. Därefter förbättras och förändras designen baserat på återkopplingen från testet.

The aim of the diploma thesis is to map the information provided by web browsers, which can be used in practice to identify users on websites. The work focuses on obtaining and subsequent analysis of information about devices, browsers and side effects caused by web extensions that mask the identity of users. The acquisition of information is realized by a designed and implemented library in the TypeScript language, which was deployed on 4 commercial websites. The analysis of the obtained information is carried out after a month of operation of the library and focuses on the degree of information obtained, the speed of obtaining information and the stability of information. The dataset shows that up to 94 % of potentially different users have a unique combination of information. The main contribution of this work lies in the created library, design of new methods of obtaining information, optimization of existing methods and the determination of quality and poor quality information based on their level of information, speed of acquisition and stability over time.