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ItemConcept-based Decision Tree ExplanationsMutahar, Gayda Mohameed Q. ( 2021)This thesis evaluates whether training a decision tree based on concepts extracted from a concept-based explainer can increase interpretability for Convolutional Neu- ral Networks (CNNs) models and boost the fidelity and performance of the used explainer. CNNs for computer vision have shown exceptional performance in crit- ical industries. However, it is a significant barrier when deploying CNNs due to their complexity and lack of interpretability. Recent studies to explain computer vision models have shifted from extracting low-level features (pixel-based expla- nations) to mid-or high-level features (concept-based explanations). The current research direction tends to use extracted features in developing approximation al- gorithms such as linear or decision tree models to interpret an original model. In this work, we modify one of the state-of-the-art concept-based explanations and propose an alternative framework named TreeICE. We design a systematic evaluation based on the requirements of fidelity (approximate models to origi- nal model’s labels), performance (approximate models to ground-truth labels), and interpretability (meaningful of approximate models to humans). We conduct computational evaluation (for fidelity and performance) and human subject ex- periments (for interpretability). We find that TreeICE outperforms the baseline in interpretability and generates more human-readable explanations in the form of a semantic tree structure. This work features how important to have more understandable explanations when interpretability is crucial.
ItemLearned Hashmap for Spatial QueriesHaozhan, Shi ( 2020)Spatial indexes such as R-Tree are widely used for managing spatial objects data efficiently, which is influenced by the popular one-dimensional range index B-Tree. Research has suggested that applying machine learning techniques such as linear regression or a neural network can improve the performance of traditional data structures. However, most studies are focused on tuning recursive learned models or learning a different ordering of data items. Many of them cannot guarantee query accuracy as in the traditional methods. This study investigates a different approach to the learned spatial index, namely Learned Spatial Hashmap (LSPH), which combines the learned model and hashmap. It only requires values from one of the data dimensions to build. Results from experiments on both synthetic and real-world datasets show that our approach significantly reduces the query processing time and maintains 100% accuracy, which is more efficient than traditional spatial indexes and more robust than recently proposed learned spatial indexes.
ItemPredicting Students' Intention to Use Gamified Mobile Learning in Higher EducationAlsahafi, Roaa Abdulaziz Ali ( 2020)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.