Computing and Information Systems - Theses
Now showing items 1-12 of 422
More than digital Lego: A study of children's play in Minecraft
This thesis is about children’s digital play and the contexts in which it occurs. I have illuminated and explored how digital play sits within broader discourses, family contexts and technological trends that make for a sociocultural context surrounding digital play that in many ways sets it apart from ‘proper’ play. The aim of this thesis was to describe and understand children’s digital play through an investigation of Minecraft. Minecraft, now a decade old, has come to occupy a position of significance in the playworlds of many children. However, as with children’s digital gameplay in general, research has tended to look for outcomes associated with play rather than what Minecraft play is. As well as being popular, Minecraft is uniquely positioned within media and academic discourses, as a game especially associated with learning and creativity but still a form of ‘screen time’. This thesis consists of three studies. In study 1, using a discourse analysis of social media comments, I provided evidence of a persistent ambivalence around children and ‘screen time’ which contributes to the broad context of everyday digital play. In study 2, a survey of 753 parents, both quantitative and qualitative data demonstrated the popularity of Minecraft among children and presented parent views of Minecraft. In study 3, an ethnographically informed study of 10 families, I provide detailed descriptions of moments of Minecraft play. These findings present a disconnect between adult constructions of children’s digital play practices and children’s actual play practices which may have ramifications for upholding children’s right to access opportunities for play.
Intelligent Scaling of Container-based Web Applications in Geographically Distributed Clouds
Cloud data centers are increasingly distributed around the globe. Recently, containerisation, a lightweight virtualization technology, has been rapidly adopted as an application packaging mechanism for efficient, consistent web application deployment and scaling within and across Cloud-based data centers. To leverage Cloud elasticity and scalability, containers commonly run on elastic, scalable clusters of virtual machines (VMs). Such global infrastructure and lightweight deployment capabilities offer a perfect choice for deploying latency-sensitive web applications in multiple locations to serve globally distributed users. However, managing container-based web applications, including containers and VMs, in widely dispersed data centers currently lacks intelligent deployment and elasticity capabilities from Cloud providers. This thesis investigates several problems related to the lack of such capabilities. This includes problems of deployment such as where and how to deploy VM clusters as well as geo-replicated application containers across data centers to address potential outages while considering wide-area network latency issues. It also considers how to dynamically deploy clusters across data centers to handle potential spatial workload fluctuations with minimum costs. This in turn gives rise to elasticity problems for multi-cluster container-based web applications deployed to multiple data centers. These problems include how to rapidly scale overloaded clusters at the VM level through temporary inter-cluster resource utilisation to avoid Cloud VM provisioning delays. Ideally this should provide sufficient VM resources for the timely launching of new containers in response to sudden workload spikes and avoid costly resource over-provisioning. A further challenge is how to control elastic scaling for both containers and VMs while considering application-level metrics and potential variations in container processing capacity, due to performance interference in shared Cloud data centers. Key to this is the need to optimise performance, availability and costs in a flexible and intelligent manner. This thesis aims to enhance the state-of-the-art in the deployment and elasticity of container-based web applications in geographically distributed Cloud environments, by tacking the above-mentioned problems using meta-heuristics and queuing theory. The thesis makes the following key contributions: 1. it provides an approach for latency-aware failover deployment of container-based web applications across multiple Cloud-based data centers to maintain performance with associated SLOs under normal conditions and in the presence of failures; 2. it provides an approach for dynamic elastic deployment of container-based clusters, both in terms of the quantity and placement across data centers whilst offering trade offs between cost and performance in the context of geographic web workload changes; 3. it offers a cost-efficient, rapid auto-elastic scaling approach for bursty multi-cluster container-based web applications deployed across data centers that scales containers in overloaded situations in a timely and cost-efficient fashion; 4. it presents a two-level elasticity controller algorithm that seamlessly auto-scales at both the container and VM levels based on application-level metrics and queuing-based performance models through estimating the container capacity needed without violating SLOs; 5. it supports dynamic, inter-data center latency aware container scheduling policies for cross-data center clusters that are able to optimise the overall performance, and 6. it presents extensive experiments using case studies based on the container technologies Docker, Docker-Swarm and Kubernetes on the Australia-wide distributed Cloud computing environment (NeCTAR) and international (commercial) cloud data centres.
Constructing authentic esports spectatorship: An ethnography
Formally organised competitive video gaming, commonly known as esports, has seen a rapid rise in popularity over the past decade as a form of spectator entertainment. Thousands of fans pack grand stadia across the globe to watch their favourite esports teams and players compete for extravagant prize pools, while millions watch online remotely in their homes via livestreaming platforms like Twitch. Unlike conventional sports however, there is no inherent need for esports to take place in any particular physically-situated site like a stadium. The computerised nature of esports lends the practice a perceived placeless quality. No matter where an esports event is situated, be it in a stadium or networked across homes, the digital synthetic environments in which players’ avatars compete remain the same as objective constructs of code and graphical assets. If the perspective of watching esports is largely the same across different sites by virtue of its inherent mediation, then why is esports spectated beyond the comfort of the home? The aim of this thesis is to address this conundrum by exploring the experiences esports spectatorship in varying contexts. In particular, this thesis seeks to understand how experiences of esports spectatorship are influenced by and differ across various sites of spectatorship. This aim is achieved through an ethnographic methodology, where data is generated through embedded interactions and experiences with esports spectators at three distinct sites of esports spectatorship. Data generation methods including semi-structured interviews and various forms of participant observation are employed across the ethnographic fieldwork, while data analysis is largely conducted through reflexive thematic analysis and a thick description approach. Three studies are conducted, each looking at a separate site of esports spectatorship: the home, the stadium, and the esports bar. Study 1 focuses on the experiences of domestic esports spectatorship. The findings of the study demonstrate that in the mundanity of the home, domestic spectators perform laborious spectatorship to authenticate and make spectacular their experiences of spectating esports. It also demonstrates that despite a perceived sense of autonomy, domestic spectators commonly encounter numerous compromising factors in their homes which often prevents them from constructing an ideal spectating experience. Study 2 focuses on experiences of and motivations for esports spectatorship in stadia. It reveals that spectators seek to affirm their expectations of authentic esports by attending events held in stadia, thus establishing notions of esporting authenticity. Besides seeking to partake in an authentic experience of esports spectatorship, those attending stadium-situated events seek to present themselves as authentic esports spectators. Aware of their status as props in the mediated spectacle of esports events broadcast to remote audiences, those spectating in stadia seek to present themselves in a perceived authentic manner to convince event organisers to host future esports events in Australia. Study 3 focuses on the experiences of spectating esports in an esports bar, representing a site of public communal spectatorship between the stadium and the home. Despite being a public place, the bar is in many ways more homely than the home. Void of many compromising factors commonly found in domestic environments, spectators at the esports bar are free to exercise a greater degree of autonomy over the construction of authentic esports spectatorship experiences. Taken together, the three studies reveal ways in which esports spectators construct authenticity in their experiences of spectatorship by creating a sense of placefulness. In doing so they establish a convention of esports authenticity for both those within and outside of the esports fandom. Different sites of spectatorship offer different tools, resources, and opportunities to construct experiences of esports spectatorship. Spectators choose accesible sites that best allow them to construct what they percieve an experience of esports spectatorship ought to be.
Machine Learning-based Energy and Thermal Efficient Resource Management Algorithms for Cloud Data Centres
Cloud data centres are the backbone infrastructures of modern digital society and the economy. Data centres have witnessed tremendous growth, consuming enormous energy to power IT equipment and cooling system. It is estimated that the data centres consume 2% of global electricity generated, and the cooling system alone consumes up to 50% of it. Therefore, to save significant energy and provide reliable services, workloads should be managed in both an energy and thermal efficient manner. However, existing heuristics or static rule-based resource management policies often fail to find an optimal solution due to the massive complexity and non-linear characteristics of the data centre and its workloads. In this thesis, we focus on machine learning-based resource management algorithms for energy and thermal efficiency in Cloud data centres which are proven to be efficient in capturing non-linearity between interdependent parameters. We explore how these techniques can be adapted to resource management problems to increase the energy and thermal efficiency of Cloud data centres while simultaneously satisfying application QoS requirements. In particular, we propose algorithms for workload placement, consolidation, application scheduling, and configuring efficient frequencies of resources in Cloud data centres. The proposed solutions are evaluated using various simulation toolkits and prototype systems implemented on real testbeds.
Safe acceptance of zero-confirmation transactions in Bitcoin
Acceptance of zero confirmation transactions in Bitcoin is inherently unsafe due to the lack of consistency in states between nodes in the network. As a consequence of this, Bitcoin users must endure a mean wait time of 10 minutes to accept confirmed transactions. Even so, due to the possibility of forks in the Blockchain, users who may want to avoid invalidation risks completely may have to wait up to 6 confirmations, which in turn results in a 60 minute mean wait time. This is untenable and remains a deterrent to the utility of Bitcoin as a payment method for merchants. Our work seeks to address this problem by introducing a novel insurance scheme to guarantee a deterministic outcome for transaction recipients. The proposed insurance scheme utilizes standard Bitcoin scripts and transactions to produce inter-dependent transactions which will be triggered or invalidated based on the occurance of potential doublespend attacks. A library to setup the insurance scheme and a test suite was implemented for anyone who may be interested in using this scheme to setup a fully anonymous and trustless insurance scheme. Based on our test in Testnet, our insurance scheme was successful at defending against 10 out of 10 doublespend attacks.
Statistical Approaches for Entity Resolution under Uncertainty
When real-world entities are referenced in data, their identities are often obscured. This presents a problem for data cleaning and integration, as references to an entity may be scattered across multiple records or sources, without a means to identify and consolidate them. Entity resolution (ER; also known as record linkage and deduplication) seeks to address this problem by linking references to the same entity, based on imprecise information. It has diverse applications: from resolving references to individuals in administrative data for public health research, to resolving product listings on the web for a shopping aggregation service. While many methods have been developed to automate the ER process, it can be difficult to guarantee accurate results for a number of reasons, such as poor data quality, heterogeneity across data sources, and lack of ground truth. It is therefore essential to recognise and account for sources of uncertainty throughout the ER process. In this thesis, I explore statistical approaches for managing uncertainty—both in quantifying the uncertainty of ER predictions, and in evaluating the accuracy of ER to high precision. In doing so, I focus on methods that require minimal input from humans as a source of ground truth. This is important, as many ER methods require vast quantities of human-labelled data to achieve sufficient accuracy. In the first part of this thesis, I focus on Bayesian models for ER, owing to their ability to capture uncertainty, and their robustness in settings where labelled training data is limited. I identify scalability as a major obstacle to the use of Bayesian ER models in practice, and propose a suite of methods aimed at improving the scalability of an ER model proposed by Steorts (2015). These methods include an auxiliary variable scheme for probabilistic blocking, a distributed partially-collapsed Gibbs sampler, and fast algorithms for performing Gibbs updates. I also propose modelling refinements, aimed at improving ER accuracy and reducing sensitivity to hyperparameters. These refinements include the use of Ewens-Pitman random partitions as a prior on the linkage structure, corrections to logic in the record distortion model and an additional level of priors to improve flexibility. I then turn to the problem of ER evaluation, which is particularly challenging due to the fact that coreferent pairs of records (which refer to the same entity) are extremely rare. As a result, estimates of ER performance typically exhibit high levels of statistical uncertainty, as they are most sensitive to the rare coreferent (and predicted coreferent) pairs of records. In order to address this challenge, I propose a framework for online supervised evaluation based on adaptive importance sampling. Given a target performance measure and set of ER systems to evaluate, the framework adaptively selects pairs of records to label in order to approximately minimise statistical uncertainty. Under verifiable conditions on the performance measure and adaptive policy, I establish strong consistency and a central limit theorem for the resulting performance estimates. I conduct empirical studies, which demonstrate that the framework can yield dramatic reductions in labelling requirements when estimating ER performance to a fixed precision.
Towards More Realistic Deep Generative Inpainting
The current era has witnessed an explosion of information, where users are dealing with an overwhelming number of media such as images and videos on their electronic devices every day. Processing and editing these media has become an increasing need in our daily life. Inpainting, one important yet challenging visual editing technique in the field of computer vision, refers to the art of restoring missing regions in a given medium (e.g., a damaged image) based on the information from undamaged regions. Although empirical inpainting techniques have existed for many years, this topic has attracted even more popularity due to the recent development in deep neural networks. Inpainting techniques have a wide range of applications in the real world and are extensively adopted in various scenarios, such as restoration of damaged images/videos, removal of unwanted objects, and loss recovery of image compression transmission. In this thesis, we focus on designing deep inpainting techniques to achieve more realistic inpainting results. To be specific, four research problems in three different inpainting scenarios are intensively studied. The first research problem aims to maintain semantic consistency from the perspective of model architecture for single image inpainting. We propose an inpainting model that is capable of using semantically interpretable information for image inpainting. The second research problem attempts to maintaining semantic consistency from the perspective of optimization for single image inpainting. We exploit an information geometric measure Local Intrinsic Dimensionality to enforce, in deep feature space, the alignment between the data submanifolds learned by an inpainting model and those of the original data. The third research problem aims to devise one of the first deep learning-based models for stereo image inpainting. We present an Iterative Geometry-Aware Cross Guidance Network, which explicitly learns the geometry correlation and alternately narrows down the missing regions of the two views in an iterative manner. The fourth research problem focuses on addressing the current challenges of video inpainting and designing an improved model. We propose a novel video inpainting network to realize effective context aggregation by exploiting both short-term and long-term reference information. Considering that deep inpainting techniques have the risk of being manipulated for image forgery, we also address the research problem of deep inpainting detection to combat inpainting-based image forgery. Specifically, we make the first attempt towards universal detection of deep image inpainting, where a detection network trained on one single type of training data can detect a wide range of deep image inpainting methods.
Distributed data stream processing and task placement on edge-cloud infrastructure
Indubitable growth of smart and connected edge devices with substantial processing power has made ubiquitous computing possible. These edge devices either produce streams of information related to the environment in which they are deployed or the devices can be located in proximity to such information producers. Distributed Data Stream Processing is a programming paradigm that is introduced to process these event streams to acquire relevant insights in order to make informed decisions. While deploying data stream processing frameworks on distributed cloud infrastructure has been the convention, for latency critical real-time applications that rely on data streams produced outside the cloud on the edge devices, the communication overhead between the cloud and the edge is detrimental. The privacy concerns surrounding where the data streams are processed is also contributing to the move towards utilisation of the edge devices for processing user-specific data. The emergence of Edge Computing has helped to mitigate these challenges by enabling to execute processes on edge devices to utilise their unused potential. Distributed data stream processing that shares edge and cloud computing infrastructure is a nascent field which we believe to have many practical applications in the real world such as federated learning, augmented/virtual reality and healthcare applications. In this thesis, we investigate novel modelling techniques and solutions for sharing the workload of distributed data stream processing applications that utilise edge and cloud computing infrastructure. The outcome of this study is a series of research works that emanates from a comprehensive model and a simulation framework developed using this model, which we utilise to develop workload sharing strategies that consider the intrinsic characteristics of data stream processing applications executed on edge and cloud resources. First, we focus on developing a comprehensive model for representing the inherent characteristics of data stream processing applications such as the event generation rate and the distribution of even sizes at the sources, the selectivity and productivity distribution at the operators, placement of tasks onto the resources, and recording the metrics such as end-to-end latency, processing latency, networking latency and the power consumption. We also incorporate the processing, networking, power consumption, and curating characteristics of edge and cloud computing infrastructure to the model from the perspective of data stream processing. Based on our model, we develop a simulation tool, which we call ECSNeT++, and verify its accuracy by comparing the latency and power consumption metrics acquired from the calibrated simulator and a real test-bed, both of which execute identical applications. We show that ECSNeT++ can model a real deployment, with proper calibration. With the public availability of ECSNeT++ as an open source software, and the verified accuracy of our results, ECSNeT++ can be used effectively for predicting the behaviour and performance of stream processing applications running on large scale, heterogeneous edge and cloud computing infrastructure. Next, we investigate how to optimally share the application workload between the edge and cloud computing resources while upholding quality of service requirements. A typical data stream processing application is formed as a directed acyclic graph of tasks that consist of sources that generate events, operators that process incoming events and sinks that act as destinations for event streams. In order to share the workload of such an application, these tasks need to placed onto the available computing resources. To this end, we devise an optimisation framework, consisting of a constraint satisfaction formulation and a system model, that aims to minimise end-to-end latency through appropriate placement of tasks either on cloud or edge devices. We test our optimisation framework using ECSNeT++, with realistic topologies and calibration, and show that compared to edge-only and cloud-only placements, our framework is capable of achieving 8-14% latency reduction and 14-15% energy reduction when compared to the conventional cloud only placement, and 14-16% latency reduction when compared to a naive edge only placement while also reducing the energy consumption per event by 1-5%. Finally, in order to cater the multitude of applications that operate under dynamic conditions, we propose a semi-dynamic task switching methodology that can be applied to optimise end-to-end latency of the application. Here, we approach the task placement problem for changing environment conditions in two phases: in the first phase respective locally optimal task placements are acquired for discrete environment conditions which are then fed to the second phase, where the problem is modelled as an Infinite Horizon Markov Decision Process with discounted rewards. By solving this problem, an optimal policy can be obtained and we show that this optimal policy can improve the performance of distributed data stream processing applications when compared with a dynamic greedy task placement approach as well as static task placement. For real-world applications executed on ECSNeT++, our approach can improve the latency as much as 10 - 17% on average when compared to a fully dynamic greedy approach.
Task assignment using worker cognitive ability and context to improve data quality in crowdsourcing
While crowd work on crowdsourcing platforms is becoming prevalent, there exists no widely accepted method to successfully match workers to different types of tasks. Previous work has considered using worker demographics, behavioural traces, and prior task completion records to optimise task assignment. However, optimum task assignment remains a challenging research problem, since proposed approaches lack an awareness of workers' cognitive abilities and context. This thesis investigates and discusses how to use these key constructs for effective task assignment: workers' cognitive ability, and an understanding of the workers' context. Specifically, the thesis presents 'CrowdCog', a dynamic online system for task assignment and task recommendations, that uses fast-paced online cognitive tests to estimate worker performance across a variety of tasks. The proposed task assignment method can achieve significant data quality improvements compared to a baseline where workers select preferred tasks. Next, the thesis investigates how worker context can influence task acceptance, and it presents 'CrowdTasker', a voice-based crowdsourcing platform that provides an alternative form factor and modality to crowd workers. Our findings inform how to better design crowdsourcing platforms to facilitate effective task assignment and recommendation, which can benefit both workers and task requesters.
Predicting Students' Intention to Use Gamified Mobile Learning in Higher Education
Background and Objectives: While gamified mobile learning holds the potential to offer an interactive learning environment that can improve students’ engagement, the predictors of its adoption remain underexplored, especially in a higher education context. The aim of this study, therefore, is threefold: (i) to identify predictors of higher education students’ intention to use gamified mobile learning; (ii) to examine the correlations among these predictors; (iii) to explore students’ attitudes towards different game elements. Methods: For the first and second objectives, the study extended the Unified Theory of Acceptance and Use of Technology (UTAUT) with cognitive gratification and perceived enjoyment. For the third objective, the study explored students’ attitudes towards five popular game elements in gamifying learning systems; these are Points, Levels, Leaderboard, Teams, and Gifting. A total of 440 responses were collected from higher education students from different regions of Saudi Arabia, using Qualtrics survey tool. After conducting data screening, 271 valid responses were considered in the analysis of Structural Equation Modeling (SEM), particularly in the items of the hypothesised model, using AMOS 27. For the third research objective, 399 valid responses were obtained and analysed, using SPSS 27. Results: Our findings reveal that perceived enjoyment (β= .507, p < .001) and social influence (β= .261, p < .001) had the strongest positive effects on intention to use gamified mobile learning, followed by performance expectancy (β= .179, p= .008) and effort expectancy (β= .138, p= .034), while cognitive gratification had no influence (β= -.020, p= .770). The proposed model was able to explain 71% of the data variance in usage intentions. For the third objective, the results showed that game elements that allow students to quantify their achievements as individuals, i.e., Points and Levels, are the most favourable, while there seemed to be high variation with the one that encourages competition, Leaderboard, especially among female groups. Lastly, the elements that encourage collaboration, Teams and Gifting, received the lowest positive perceptions. Originality: The original contribution of this study is the empirically backed impact of the extended UTAUT on students’ intention to use gamified mobile learning in higher education. It also contributes in shedding light on which game elements are most promising in this context. The study offers a set of practical outcomes to contribute to realising successful adoption of gamified mobile learning in higher education.
Efficient algorithms to improve feature selection accuracy in high dimensional data
Feature selection plays a vital role in machine learning by removing the features which are irrelevant for the learning task in high dimensional datasets. Selecting the relevant features before the learning task often improves the prediction accuracy of the learning models, reduces the training and prediction times of the learning models and results in simple learning models. However, selecting the optimal feature subset is an NP-hard problem and achieving high prediction accuracy with low computational costs remains a challenge. In this thesis, we develop efficient feature selection algorithms which gain high prediction accuracy in machine learning tasks to address this problem. First, we propose a method to improve the feature selection accuracy in multi-class binary data compared to state-of-the-art methods for binary feature selection. Despite many feature selection methods specifically designed for different data types, only a few methods are specifically designed for binary data. These few methods also result in poor class separation; therefore, low prediction accuracy in multi-class binary datasets. In our feature selection method, we first propose two new feature selection measures which exploit the special properties in binary data. Second, we propose a feature selection algorithm, which uses these measures to achieve good class separation in multi-class binary data. We experimentally show that compared to the existing feature selection methods, the proposed method achieves high classification accuracy in multi-class binary datasets with reasonable computational costs. The proposed method has wide applicability in domains such as image and gene analysis and text classification in which binary datasets are commonly used. Second, we propose a framework, which facilitates the supervised filter feature selection algorithms to exploit feature group information from external sources of knowledge. Among the various feature selection methods available, filter methods use statistical measures, and wrapper and embedded methods use the classification accuracy of a learning model to evaluate the features. Compared to the wrapper and embedded methods, filter methods are advantageous in terms of the interpretability of the results, simplicity and computational efficiency. However, in the literature, feature group information, obtained from external sources of knowledge, is rarely used to improve the filter feature selection approach. We theoretically and experimentally show that using the proposed framework, external feature group information can be incorporated into some filter feature selection algorithms with no additional computational costs. We also show that the new algorithm which exploits feature group information achieves higher classification accuracy than the original algorithm and other existing feature selection methods. Third, we propose a framework to incorporate feature group information from the external sources of knowledge into unsupervised feature selection methods. Due to the unavailability of the class labels, unsupervised feature selection is more challenging than supervised feature selection, yet has wide applicability in real world applications. We experimentally show that the proposed framework can be used to develop new unsupervised feature selection algorithms which achieve high clustering performance with low computational costs. As the current framework is limited to disjoint feature groups, in our fourth contribution, we extend it to exploit pairwise feature correlations as well, resulting in a more generic framework. Proposed group based and pairwise correlation based feature selection methods have wide applicability in areas such as genomic, text and image data analysis which involve datasets whose features show group behaviours and pairwise correlations.
Towards Sensor-based Learning Analytics: A Contactless Approach
Learning analytics is an emerging field in which sophisticated analytic tools are used to improve students’ learning. Driven by the data from heterogeneous resources and the latest data mining techniques, learning analytics mainly attempts at creating a more integrated and personalized learning experience for each student. Most of the studies in the field rely exclusively on keyboard-mouse interactions. Though these interactions are effective in capturing overall sentiments and reactions, they do not provide enough granularity to conduct detailed analyses about students’ learning patterns. This dissertation will provide an interdisciplinary perspective on sensor-based learning analytics by discussing data-science techniques, HCI-based solutions in relation to educational theories. The thesis begins by providing an overview of the field of learning analytics and discusses the limitations of traditional contact-based sensor technologies in education research. We then discuss how data generated from contactless sensors such as an eye-tracker and a thermal camera can provide an informative window on students’ learning experiences. By conducting two lab-based studies, we analysed students’ eye movements and facial temperature to answer our four research questions related to predicting students’ desktop activities, evaluating learning task design, measuring students’ cognitive load, and measuring students’ attention patterns while students’ perform a learning task in a digital learning environment. The first study investigates the role of gaze-based features in predicting the desktop activities of the students. The outcomes of the study reveal that gaze-based features can be used to predict desktop activities of the students, and the design of a novel set of features (mid-level gaze features) can improve the accuracy of the prediction. The study lacks strong ground truth and contains a small sample size with only one sensor data. The limitations are addressed by the second study, which is a more extensive study and utilises a multi-sensor setup consisting of an eye-tracker, a thermal camera, a web camera and a physical slider. The study focuses on understanding students’ learning process in video-based learning. The data is analysed at different granularity, and results are present as separate chapters in the thesis. The first analysis compares two types of instructional designs of the video lectures (text vs. animation) using a physical slider. The results suggest that continuous evaluation of video lectures using a slider provides deeper insight into how students experience video lectures. To confirm these findings using a physiological marker, we again compare different instructional designs of the video lecture by measuring the cognitive load of the students using a thermal camera. The results suggest that thermal imaging is a reliable psycho-physiological indicator of cognitive load, and text-based video lectures induce higher cognitive load than animation-based video lectures. The final analysis focuses on measuring the coattention between students’ attention and instructor’s dialogues by measuring how much student follows instructor dialogues in a video lecture using an eye-tracker. The results suggest that students’ attention mostly follows the instructor’s dialogues on the screen, and their direction of attention is influenced by their prior knowledge. The thesis contributes to the fields of learning analytics by combining knowledge from different disciplines – HCI-design based on contactless sensors, video lectures derived from educational psychology, and novel data science techniques to extract features from eye movements and facial temperature to understand students’ learning process in a digital learning environment. The thesis’s final chapter discusses these contributions and provides future directions for sensor-based learning analytics using contactless sensors.