Drill and practice systems provide students with an informal learning environment to learn programming languages. In a traditional classroom setting, while feedback is personalized for each individual, it is a time consuming process. These online environments possess the ability to provide instantaneous feedback and can be accessed from any location. However, while these are conveniences, there is still an issue with the quantity and quality of feedback that is provided to each user by the system, and whether it is helpful towards helping them solve the exercise with a large understanding of the concept being tested. In this thesis we investigate how students perceive additional feedback would help them in completing coding exercises in CodeWorkout. We conducted these investigations through user studies, across two focus groups, with Computer Science students from various years. The study was conducted over one semester with a total of seventeen participants. A discussion based frequently asked questions (FAQ) tool, the ability to request a hint during submissions and the option to provide a hint to other users, to encourage active learning, were all options presented to participants during these focus groups. The information gathered though these group discussions formed the basis of our conclusion and implications. The overall feedback on all three tools was both positive and constructive. The idea of having a less traditional FAQ tool, complete anonymity in responses, as well as the ability to vote on hints provided were strong emergent themes through the study. The majority of Participants felt that they would utilize all these tools in some fashion, were they provided, and would find them helpful in completing a coding exercise if they were stuck. Lastly, we conclude with suggestions for potential design and feature options for the system. / Master of Science

abstract: Lots of previous studies have analyzed human tutoring at great depths and have shown expert human tutors to produce effect sizes, which is twice of that produced by an intelligent tutoring system (ITS). However, there has been no consensus on which factor makes them so effective. It is important to know this, so that same phenomena can be replicated in an ITS in order to achieve the same level of proficiency as expert human tutors. Also, to the best of my knowledge no one has looked at student reactions when they are working with a computer based tutor. The answers to both these questions are needed in order to build a highly effective computer-based tutor. My research focuses on the second question. In the first phase of my thesis, I analyzed the behavior of students when they were working with a step-based tutor Andes, using verbal-protocol analysis. The accomplishment of doing this was that I got to know of some ways in which students use a step-based tutor which can pave way for the creation of more effective computer-based tutors. I found from the first phase of the research that students often keep trying to fix errors by guessing repeatedly instead of asking for help by clicking the hint button. This phenomenon is known as hint refusal. Surprisingly, a large portion of the student's foundering was due to hint refusal. The hypothesis tested in the second phase of the research is that hint refusal can be significantly reduced and learning can be significantly increased if Andes uses more unsolicited hints and meta hints. An unsolicited hint is a hint that is given without the student asking for one. A meta-hint is like an unsolicited hint in that it is given without the student asking for it, but it just prompts the student to click on the hint button. Two versions of Andes were compared: the original version and a new version that gave more unsolicited and meta-hints. During a two-hour experiment, there were large, statistically reliable differences in several performance measures suggesting that the new policy was more effective. / Dissertation/Thesis / M.S. Computer Science 2011

Internet applications have varied Quality of Service (QoS) Requirements. Traditional applications such as FTP and email are throughput sensitive since their quality is primarily affected by the throughput they receive. There are delay sensitive applications such as streaming audio/video and IP telephony, whose quality is more affected by the delay. The current Internet however does not provide QoS support to the applications and treats the packets from all applications as primarily throughput sensitive. Delay sensitive applications can however sacrifice throughput for delay to obtain better quality. We present a Traffic Sensitive QoS controller (TSQ) which can be used in conjunction with many existing Active Queue Management (AQM) techniques at the router. The applications inform the TSQ enabled router about their delay sensitivity by embedding a delay hint in the packet header. The delay hint is a measure of an application's delay sensitivity. The TSQ router on receiving packets provides a lower queueing delay to packets from delay sensitive applications based on the delay hint. It also increases the drop probability of such applications thus decreasing their throughput and preventing any unfair advantage over throughput sensitive applications. We have also presented the quality metrics of some typical Internet applications in terms of delay and throughput. The applications are free to choose their delay hints based on the quality they receive. We evaluated TSQ in conjunction with the PI-controller AQM over the Network Simulator (NS-2). We have presented our results showing the improvement in QoS of applications due to the presence of TSQ.

Flow state is a state of intense focus and engagement, which is fulfilling for people experiencing it and therefore generally sought-after by creators of virtual games and experiences. Virtual Reality (VR) is known to be one of the most immersive technologies available today. As such, it has great potential to foster flow state in its users. This work tries to determine which design characteristics favor flow the most, with regard to providing guidance to the user. In particular, this research examines whether in a VR escape room, text hints or glowing cues are more effective to foster feelings of flow. The research process included design and implementation of the VR escape room from the ground up, to create the perfect setting for the experiments, a VR experience in which ultimate goal and intermediate steps are precisely defined. This allows to give relevant hints every step of the experience, and to examine the effects of hints design on flow. The intensity of the resulting flow state has been evaluated through the Activity Flow State Scale (AFSS) designed by Payne et al., and qualitative data has also been gathered, from direct observation and user comments. AFSS score, duration of the VR experience, and duration estimated by the users, have been compared between the version of the VR escape room which presents hints as text prompts and the one in which relevant objects glow. This data has then been connected to findings resulting from the thematic analysis of direct observation and user comments. Lastly, general insights and guidelines have been extracted, to inform designers and game developers willing to create flow-oriented VR experiences. / “Flow state” är ett tillstånd av intensivt fokus och engagemang, vilket är tillfredsställande för dem som upplever det och är därför eftertraktat av skapare av virtuella spel och upplevelser. Virtual Reality (VR) är känd som en av de mest immersiva tekniker som finns tillgänglig idag. Som sådan har den stor potential att främja “flow state” hos sina användare. Detta arbetet försöker fastställa vilka designegenskaper som gynnar “flow” mest, när det gäller att ge vägledning till användaren i en VR-upplevelse. Arbetet undersöker om det i ett VR-”escape room” är effektivare att ge användaren tips i text eller som lysande signaler för att främja känslan av flow. Forskningsprocessen omfattade utformning och implementation av ett VR-”escape room” från grunden för att skapa den perfekta miljön för experimenten, en VR-upplevelse där slutmålet och de mellanliggande stegen är exakt definierade. Detta gör det möjligt att ge relevanta tips i varje steg av upplevelsen och att undersöka effekterna av tipsens utformning på flödet. Intensiteten i ett resulterande “flow state”har utvärderats med hjälp av Activity Flow State Scale (AFSS) som utformats av Payne m.fl. och kvalitativa data har också samlats in från direkta observationer och användarkommentarer. AFSS-poäng, VR-upplevelsens varaktighet och användarnas uppskattade varaktighet har jämförts mellan två versioner av ett VR-”escape room”. Ett där tips presenteras som textmeddelanden och ett där relevanta objekt lyser. Datan har sedan kopplats samman med resultaten från den tematiska analysen av direkta observationer och användarkommentarer. Slutligen har allmänna insikter och riktlinjer tagits fram för att informera designers och spelutvecklare som vill skapa “flow state”-orienterade VR-upplevelser.

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While receiving hints is an essential part of playing an escape room, most systems don'talign with the room's overall theme. Therefore, it will have a large impact on the player’sexperience of the game. This thesis explores developing a new hint system in escaperooms within the theory of environmental storytelling. A literature review and a surveyshowed the current field of hint systems where the need of the players to receive hintsand how providing hints to the player needs to change were concluded in designrequirements. These requirements recommend incorporating the storyline in diAerentstages of environmental storytelling for a deeper connection with the narrative. Thishelps the players to engage in a flow state and provides a more enjoyable experience.As a result, two artefacts are created to experience the hint system based on therequirements in a playtest. The exploration showed that players have a more enjoyableexperience when the hint system is included in the storyline and adjusted to their needsby engaging in a flow state.

The focus of this study is the development and evaluation of a new partially supervised learning framework. This framework belongs to an emerging field in machine learning that augments unsupervised learning processes with some elements of supervision. It is based on proximity fuzzy clustering, where an active learning process is designed to query for the domain knowledge required in the supervision. Furthermore, the framework is extended to the parametric optimization of the kernel function in the proximity fuzzy clustering algorithm, where the goal is to achieve interesting non-spherical cluster structures through a non-linear mapping. It is demonstrated that the performance of kernel-based clustering is sensitive to the selection of these kernel parameters. Proximity hints procured from domain knowledge are exploited in the partially supervised framework. The theoretic developments with proximity fuzzy clustering are evaluated in several interesting and practical applications. One such problem is the clustering of a set of graphs based on their structural and semantic similarity. The segmentation of music is a second problem for proximity fuzzy clustering, where the aim is to determine the points in time, i.e. boundaries, of significant structural changes in the music. Finally, a time series prediction problem using a fuzzy rule-based system is established and evaluated. The antecedents of the rules are constructed by clustering the time series using proximity information in order to localize the behavior of the rule consequents in the architecture. Evaluation of these efforts on both synthetic and real-world data demonstrate that proximity fuzzy clustering is well suited for a variety of problems. / Digital Signals and Image Processing

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Des études soutiennent que l’entraînement cognitif est une méthode efficace pour ralentirle déclin cognitif chez les personnes âgées. Les jeux sérieux basés sur la réalité virtuelle(RV) ont trouvé une application dans ce domaine en raison du haut niveau d’immersionet d’interactivité offert par les environnements virtuels (EV). Ce projet implémente unjeu d’orientation 3D en réalité virtuelle entièrement immersif avec un système pour guiderl’utilisateur en temps réel. Le jeu d’orientation 3D est utilisé comme exercice pour entraînerles capacités cognitives des utilisateurs. Les effets immédiats du jeu d’orientation sur lescapacités de mémoire et d’attention ont été étudiés sur quinze personnes âgées présentant undéclin cognitif subjectif (DCS). Il a été observé que bien qu’il n’y ait pas eu d’améliorationsignificative des résultats pour les exercices d’attention, les participants ont obtenu demeilleurs résultats aux exercices de mémoire spécifiques après avoir joué au jeu d’orientation. Le manque de succès dans la réalisation de l’objectif requis peut parfois augmenter lesémotions négatives chez les êtres humains, et plus particulièrement chez les personnes quisouffrent de déclin cognitif. C’est pourquoi le jeu a été équipé d’un système de guidageavec indices de localisation en temps réel pour contrôler les émotions négatives et aiderles participants à accomplir leurs tâches. Le système de guidage est basé sur des règleslogiques; chaque indice est délivré si une condition spécifique est remplie. Le changement desémotions des participants a montré que les indices sont efficaces pour réduire la frustration,étant donné qu’ils sont facilement compréhensibles et conçus pour donner un retour positif. La dernière partie du projet se concentre sur le système de guidage et met en oeuvre unmoyen pour l’activer entièrement selon les émotions d’une personne. Le problème consisteà identifier l’état des émotions qui devraient déclencher l’activation du système de guidage.Ce problème prend la forme d’un processus de décision markovien (PDM), qui peut êtrerésolu via l’apprentissage par renforcement (AR). Le réseau profond Q (RPQ) avec relectured’expérience (ER), qui est l’un des algorithmes d’apprentissage par renforcement les plusavancés pour la prédiction d’actions dans un espace d’action discret, a été utilisé dans cecontexte. L’algorithme a été formé sur des données d’émotions simulées, et testé sur les données de quinze personnes âgées acquises lors d’expériences menées dans la première partiedu projet. On observe que la méthode basée sur l’AR est plus performante que la méthodebasée sur les règles pour identifier l’état mental d’une personne afin de lui fournir des indices. / Studies support cognitive training as an efficient method to slow the cognitive declinein older adults. Virtual reality (VR) based serious games have found application in thisfield due to the high level of immersion and interactivity offered by virtual environments(VE). This project implements a fully immersive 3D virtual reality orientation game with areal-time guidance system to be used as an exercise for cognitive training. The immediateaftereffects of playing the orientation game on memory and attention abilities were studiedon fifteen older adults with subjective cognitive decline (SCD). It was observed that whilethere was no significant improvement in attention exercises, the participants performedbetter in specific memory exercises after playing the orientation game. Sometimes lack of success in achieving the required objective may increase the negativeemotions in humans and more so in people who suffer from cognitive decline. Hence, thegame was equipped with a real-time guidance system with location hints to control negativeemotions and help participants to complete the tasks. The guidance system is based onlogical rules; each hint is delivered if a specific condition is met. Change in emotions ofparticipants showed that hints are effective in reducing frustration, given that the hints areeasily comprehensible and designed to give positive feedback. The final part of the project focuses on the guidance system and implements a way toactivate it entirely based on a person’s emotions. The problem calls for identifying the stateof the emotions that should trigger the guidance system’s activation. This problem takes theform of a Markov decision process (MDP), which can be solved by setting it in a reinforcementlearning framework. Deep Q-Learning network (DQN) with experience replay (ER),which is one of the state-of-the-art reinforcement learning algorithms for predicting actionsin discrete action space, was used in this context. The algorithm was trained on simulateddata of emotions and tested on the data of fifteen older adults acquired in experimentsconducted in the first part of the project. It is observed that the RL based method performsbetter than the rule-based method in identifying the mental state of a person to provide hints.