LEE- Bilgisayar Mühendisliği Lisansüstü Programı
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Sustainable Development Goal "Goal 4: Quality Education" ile LEE- Bilgisayar Mühendisliği Lisansüstü Programı'a göz atma
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ÖgeArtificial intelligence based and digital twin enabled aeronautical AD-HOC network management(Graduate School, 2022-12-20) Bilen, Tuğçe ; Canberk, Berk ; 504172508 ; Computer EngineeringThe number of passengers using aircraft has been increasing gradually over the following years. With the increase in the number of passengers, significant changes in their needs have been made. In-flight connectivity (IFC) has become a crucial necessity for passengers with the evolving aeronautical technology. The passengers want to connect to the Internet without interruption regardless of their location and time. The aeronautical networks attract the attention of both industry and academia due to these reasons. Currently, satellite connectivity and air-to-ground (A2G) networks dominate existing IFC solutions. However, the high installation/equipment cost and latency of the satellites reduce their efficiency. Also, the terrestrial deployment of A2G stations reduces the coverage area, especially for remote flights over the ocean. One of the novel solutions is the Aeronautical Ad-hoc Networks (AANETs) to satisfy the IFC's huge demand by also solving the defects of satellite and A2G connectivities. The AANETs are based on creating air-to-air (A2A) links between airplanes and transmitting packets over these connections to enable IFC. The AANETs dramatically increase the Internet access rates of airplanes by widening the coverage area thanks to these established A2A links. However, the mobility and atmospheric effects on AANETs increase the A2A link breakages by leading to frequent aircraft replacement and reducing link quality. Accordingly, the mobility and atmospheric effects create the specific characteristics for AANETs. More specifically, the ultra-dynamic link characteristics of high-density airplanes create an unstructured and unstable topology in three-dimensional space for AANETs. To handle these specific characteristics, we first form a more stable, organized, and structured AANET topology. Then, we should continuously enable the sustainability and mapping of this created AANET topology by considering broken A2A links. Finally, we can route the packets over this formed, sustained, and mapped AANET topology. However, the above-explained AANET-specific characteristics restrict the applicability of conventional topology and routing management algorithms to AANET by increasing its complexity. More clearly, the AANET specific characteristics make its management challenging by reducing the packet delivery success of AANET with higher transfer delay. At that point, artificial intelligence (AI)-based solutions have been adapted to AANET to cope with the high management complexity by providing intelligent frameworks and architectures. Although AI-based management approaches are widely used in terrestrial networks, there is a lack of a comprehensive study that supports AI-based solutions for AANETs. Here, the AI-based AANET can take topology formation, sustainability, and routing management decisions in an automated fashion by considering its specific characteristics thanks to learning operations. Therefore, AI-based methodologies have an essential role in handling the management complexity of this hard-to-follow AANET environment as they support intelligent management architectures by also overcoming the drawbacks of conventional methodologies. On the other hand, these methodologies can increase the computational complexity of AANETs. At that point, we propose the utilization of the Digital Twin (DT) technology to handle computational complexity issues of AI-based methodologies. Based on these, in this thesis, we aim to propose an AI-based and DT-enabled management for AANETs. This system mainly consists of four main models as AANET Topology Formation Management, AANET Topology Sustainability Management, AANET Topology Mapping Management, and AANET Routing Management. Here, our first aim is to form a stable, organized, and structured AANET topology. Then, we will enable the sustainability of this formed topology. We also continuously map the formed and sustained AANET topology to airplanes. Finally, the packets of airplanes are routed on this formed, sustained, and mapped AANET topology. We will create these four models with different AI-based methodologies and combine all of them under the DT technology in the final step. In the Topology Formation Management, we will propose a three-phased topology formation model for AANETs based on unsupervised learning. The main reason for proposing an unsupervised learning-based algorithm is that we have independently located airplanes with unstructured characteristics in AANETs before forming the topology. They could be considered as the unlabeled training data for unsupervised learning. This management model utilizes the spatio-temporal locations of aircraft to create a more stable, organized, and structured AANET topology in the form of clusters. More clearly, the first phase corresponds to the aircraft clustering formation, and here, we aim to increase the AANET stability by creating spatially correlated clusters. The second phase consists of the A2A link determination for reducing the packet transfer delay. Finally, the cluster head selection increases the packet delivery ratio in AANET. In the Topology Sustainability Management, we will propose a learning vector quantization (LVQ) based topology sustainability model for AANETs based on supervised learning. The main reason for proposing a supervised learning-based algorithm is that we already have an AANET topology before the A2A link breakage, and we can use it in supervised learning for training. Accordingly, we can consider the clusters in AANET topology as a pattern; then, we can find the best matching cluster of an aircraft observing A2A link breakages through pattern classification instead of creating topology continuously. This management model works in three phases: winning cluster selection, intra-cluster link determination, and attribute update to increase the packet delivery ratio with reduced end-to-end latency. In the Topology Mapping Management, we will propose a gated recurrent unit (GRU) based topology mapping model for AANETs. In topology formation, we create AANET topology in the form of clusters by collecting airplanes having similar features under the same set. In topology sustainability, we sustain the formed clustered-AANET topology with supervised learning. However, these formed and sustained AANET topologies must be continuously mapped to the clustered airplanes to notify them about the current situation. This procedure could be considered a part of sustainability management. Here, we continuously notify the airplanes with GRU at each timestamp about topological changes. This management model works in two main parts ad forget and update gates. In Routing Management, we propose a q-learning (QLR) based routing management model for AANETs. For this aim, we map the AANET environment to reinforcement learning. Here, the QLR-based management model aims to let the airplanes find their routing path through exploration and exploitation. Accordingly, the routing algorithm can adapt to the dynamic conditions of AANETs. In this management model, we adapt the Bellman Equation to the AANET environment by proposing different methodologies for its related QLR components. Accordingly, this model mainly consists of two main parts current state & maximum state-action determination and dynamic reward determination. Therefore, we execute the topology formation, sustainability, and routing management modules through unsupervised, supervised, and reinforcement learning-based algorithms. Additionally, we take advantage of neural networks in topology mapping management. After managing the topology and routing of AANETs with AI-based models, in the DT-enabled AANET management, we will support them with the DT technology. The DT can virtually replicate the physical AANET components through closed-loop feedback in real-time to solve the computational challenges of AI-based methodologies. Therefore, we will introduce the utilization of DT technology for the AANET orchestration and propose a DT-enabled AANET (DT-AANET) management model. This model consists of the Physical AANET Twin and Controller, including the Digital AANET Twin with Operational Module. Here, the Digital AANET Twin virtually represents the physical environment. Also, the operational module executes the implemented AI-based models. Therefore, in this thesis, we aim to propose an AI-based and DT-enabled management for AANETs. In this management system, we will first aim to propose AI-based methodologies for AANET topology formation, topology sustainability, topology mapping, and routing issues. Then, we will support these AI-based methodologies with DT technology. This proposed complete management model increased the packet delivery success of AANETs with reduced end-to-end latency.
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ÖgeHierarchical deep bidirectional self-attention model for recommendation(Graduate School, 2023-05-02) İşlek, İrem ; Öğüdücü Gündüz, Şule ; 504162502 ; Computer EngineeringThis study proposes a bidirectional recommendation model to tackle the user cold start problem. We can predict the middle item when a user has only a few user-item interactions and enrich their interaction set accordingly. By recursively repeating this process, we can obtain enough interactions to make accurate item recommendations to the user. For instance, a user may buy a few items from an e-commerce site but also purchase other items from elsewhere, leading to incomplete information about their preferences. The proposed bidirectional recommendation model can fill the user's interaction history gaps, enabling accurate item recommendations even with limited data. In this thesis, we aimed to develop a recommendation system that imitates the behavior of today's e-commerce users' online purchasing experience. Our approach emphasized practicality, as we aimed to create a system that could be implemented easily in real-world e-commerce platforms. In doing so, we focused on developing an approach that could handle a large number of users and items found on such platforms while still maintaining high performance. By prioritizing these factors, we aimed to create a recommendation system that could be effectively applied in real-world scenarios. For future work, exploring combinations of the suggested algorithms for both layers would be worthwhile. Furthermore, examining the impact of algorithms proposed for the first layer or the user's shopping history enrichment algorithm on different recommendation systems would be beneficial. Ultimately, the most significant improvement is the application of proposed hierarchical recommendation network to cross-domain recommendation problems.
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ÖgeLifelong learning for auditory scene analysis(Graduate School, 2022-07-04) Bayram, Barış ; İnce, Gökhan ; 504152503 ; Computer EngineeringDue to the evolution in artificial intelligence and machine learning with the recent advancements in sensor technology, scene analysis is getting more attention for automatic sensing and understanding of dynamic environments including various targets, non-target objects, and noises. The sensory information stemming from the environments can be efficiently analyzed to infer the events, activities, and related objects. However, many issues encountered in the real world, exist that prevent robust sensing and information processing required for important real-time tasks in real dynamic environments with background noises, overlapping targets, high processing complexity, and so on. Automatic scene analysis is a major aspect of the process of collecting and extracting useful knowledge of objects and events to analyze scenes in terms of the places, situations, events, and activities. A significant number of scene analysis studies have mainly focused on visual processing approaches for the analysis of objects in various environments. Auditory Scene Analysis (ASA) has been used in various real-world tasks, which relies on the perception and analysis of stationary and non-stationary sounds from environmental events and activities, background noises, human voices, and other sound sources. In realistic environments, the dynamic spatio-temporal nature and complexity of environmental sounds, and the existence of novel events may eventually deteriorate the performance of ASA. Therefore, ASA in real-world environments is a difficult task and has not been extensively investigated. Lifelong learning that is progressively becoming a more crucial task in artificial intelligence is a continuous learning process in acquiring and adapting knowledge from dynamic environments. In this thesis, the task of lifelong ASA for Acoustic Event Recognition (AER) with Acoustic Novelty Detection (AND) is addressed to detect novel acoustic events, recognize known events, and learn in a self-learning manner. The problem is investigated by identifying and tackling various issues that may or will affect the ASA in a real-world learning environment. The main issues of lifelong learning in realistic environments are (i) existence of novel acoustic classes, (ii) existence of unlabeled data, (iii) cost of annotation, (iv) lack of adequate data for novel classes, (v) imbalanced data between classes, (vi) forgetting of previous data, and (vii) lack of memory for storing all the data and (viii) computational power for lifelong learning. In dynamic acoustic environments, the lifelong ASA for intelligent systems, agents, or robots in real-time is still an open issue. Also, recent deep learning methods for ASA have not been investigated yet while avoiding the issues of real-world environments. In this thesis, two approaches regarding the main issues, 1) a real-time ASA approach and 2) a deep learning-based ASA approach are investigated, which are able to recognize acoustic events, detect the novel events and then learn by the AER and AND models. However, both lifelong learning approaches have certain issues; which are the lack of incremental learning capability in the real-time approach for the ASA in a realistic environment, and the computational time in the deep learning-based approach. One of the main differences between the approaches is that the real-time ASA is applied to the streaming signal. Thus, each salient sound source in an acoustic scene is identified and localized by a Sound Source Localization (SSL) method to robustly perform the source-specific analysis of its signal. In addition to the SSL method, a segmentation technique is employed to segment variable-length time-series audio patterns of acoustic activities from the streaming signal to efficiently analyze the events and scenes. Moreover, the approaches differ in their audio features used for AER and AND taking into account the requirements of the algorithms and real-time processing. The first approach for lifelong learning in ASA based on a multilayered Hidden Markov Model (HMM) comprises five main steps: (1) SSL used for detection and location monitoring of the most salient sound source in a scene to perform source-specific analysis, (2) segmentation of time-series audio patterns on the streaming signal, (3) feature extraction from the segmented patterns and construction of a feature set for each pattern, (4) AER in a semi-supervised manner performed by class-specific HMMs associated with known events, (5) AND carried out using a single HMM for all the known events, from the outputs of AER module, and (6) lifelong self-learning (Chapter 4). In the step of lifelong learning, the updates of the models are realized, in which after recognizing an event, the HMM is retrained using more likely knowledge selected among all the previous and new knowledge of the event, and for a new acoustic event recently detected, a class-specific model is generated and the AND model is retrained. In Chapter 4, the offline and real-time experiments are given in detail, which are conducted using streaming signals from a real domestic environment. In the experiments, it is demonstrated that for real-time ASA, HMM for modeling the time-series audio patterns from the streaming signal is the most efficient algorithm for the AER and AND. In Chapter 5, the steps of the other proposed lifelong learning approach which is a deep learning-based approach for ASA in offline mode are explained, which are: (1) raw acoustic signal pre-processing, (2) extraction of low-level, time-varying spectral representation (spectrogram), and deep audio features, (3) AND, (4) acoustic signal augmentation, (5) AER, and (6) Incremental Class-Learning (ICL) of the audio features of the novel events. The self-learning on different types of audio features extracted from the acoustic signals of various events occurs without human supervision. For the extraction of deep audio representations, in addition to Visual Geometry Group (VGG) and Residual Neural Network (ResNet), Factorized Time-Delay Neural Network (FTDNN) and TDNN-based Long Short-Term Memory (TDNN-LSTM) networks are pre-trained using a large-scale audio dataset called Google AudioSet. As the input of the networks, Mel-Frequency Cepstral Coefficient (MFCC) and raw signals are used by F-TDNN and TDNN-LSTM, respectively, and Mel-spectrograms are taken by the VGG and ResNet. The performances of ICL with AND using Mel-spectrograms, and deep features with TDNNs, VGG, and ResNet are validated on benchmark audio datasets such as ESC-10, ESC-50, UrbanSound8K (US8K), and an audio dataset collected in a real domestic environment, also used with the proposed real-time ASA approach. The results demonstrate that the FearNet algorithm with the VGG-16 features is a more promising algorithm for incremental learning of new acoustic classes in the audio domain, and the GMM algorithm provided the best AND performances in various AND scenarios. Moreover, for the ICL with AND, the FearNet integrated with a GMM exhibits the effectiveness of scene analysis in a real-world acoustic environment to deal with novel events and recognition of unlabeled data using the ICL-based AER. The efficient performances in the experiments of AND and AER tasks are observed also using F-TDNN, and iCaRL has the ICL performances with VGG close to the performances of FearNet in ESC-10 and Domestic datasets.
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ÖgeMemory-based approaches to problems in probabilistic modeling(Lisansüstü Eğitim Enstitüsü, 2022-10-25) Akgül, Abdullah ; Ünal, Gözde ; 504201504 ; Computer EngineeringDeep neural networks are an accepted solution for many problems in deep learning; however, the application of deep neural networks to safety-critical areas such as health care is still a hot research topic. To employ deep neural networks in such fields, they are expected to fit the in-domain data set, provide calibrated predictions on problematic regions of the target domain, and separate the out-of-domain queries. Even though these expectancies are studied extensively, these studies are highly fragmented. Therefore, there is no model that is able to fit these requirements simultaneously. Continual Learning (CL) is a framework that aims to learn numerous tasks in a sequential way. The excellent CL method should adapt to new tasks perfectly without forgetting previous tasks. However, neural networks suffer from catastrophic forgetting which is a performance drop on previously learned tasks caused by the newly learned task. Yet, to get intelligent systems capable of adapting to environmental change, CL is crucial. Because of this, CL is a hot topic but the research on CL is mainly on image classification tasks and there is limited work on time sequence classification tasks. Yet, there is no work on multi-modal dynamics modeling. In this thesis, we employ an external memory to deal with problems in probabilistic modeling. Our solutions for these problems can be summarized as follows: i) Evidential Turing Processes (ETP): First, we define total calibration for the first time. After investigating two Bayesian paradigms which are the Bayesian model, and the Evidential Bayesian Model, we introduce the Complete Bayesian Model (CBM) which is a unification of those two paradigms. We develop ETP as an instance of CBMs with neural episodic memory. We build a pipeline to evaluate the models' performance for total calibration. We compare our solution, the ETP member of CBMs, with state-of-the-art members of other paradigms, and we also provide an ablation study. We investigate the models' performance under five real-world data sets including one time-series classification, and four image classification tasks. Furthermore, we tested the models in the corrupted versions of different data sets. We use four different metrics that are test error as prediction accuracy, Expected Calibration Error as in-domain calibration score, Negative Log-Likelihood (NLL) as model fit, and area under the ROC curve as out-of-domain detection score. We report that only the ETP can excel in all three aspects of total calibration simultaneously. ii) Continual Dynamic Dirichlet Process (CDDP) for Continual Learning of Multi-modal Dynamics: We introduce a new problem which is CL of multi-modal dynamics. Since the problem is novel, we create a baseline from the existing ones. For this new problem, we introduce a novel solution that employs an external memory to transfer knowledge between tasks. We curate a pipeline for this newly introduced problem, and in the pipeline new tasks are coming sequentially and each task has a certain number of different mode samples. Differences in task order may cause different results in CL setups; therefore, we change the task order for each replication. We also generate synthetic data sets and adapt time-series classification data sets to evaluate models' performance in the problem. We compare models' performance with Normalized Mean Squared Error as a measure of prediction accuracy and NLL as a measure of Bayesian model fit that quantifies uncertainty. We reveal that our approach, CDDP, compares favorably to the established parameter transfer approach in CL of multi-modal dynamical systems. To sum up, in this thesis, by experiments we show that external memory architecture can be used for both calibrations of neural networks to use in safety-critical areas and CL of multi-modal dynamics.
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ÖgeStatistical feature learning and signal generation for time-series sensor signals(Graduate School, 2022-05-31) Karakuş, Erkan ; Köse, Hatice ; 504992432 ; Computer EngineeringThe Human Activity Recognition (HAR) problem has attracted substantial attention from academia. HAR has many applications like smart home assisted living systems, healthcare monitoring systems, sports activity monitoring, and monitoring indoor and outdoor activities. HAR applications involve advanced machine learning techniques to identify and classify human activities by leveraging video cameras, wearable sensors, or any other signal like Wi-Fi or radar which eventually encodes the human activity. Human activities are encoded in signals and signal processing techniques are required to pre-process raw signals to filter out high-frequency components and to frame the signals into the fixed-length window. Wearable smart electronics are widely used in human daily life. Those smart devices contain sensors like accelerometer and gyroscope to measure triaxial acceleration and angular velocity respectively. Smartwatches, smartphones, or any such wearable sensor devices contain out-of-the-box sensors embedded in the device. Identification and classification of human activities from such signals by leveraging machine learning techniques require features to be extracted from the signal which represents the corresponding human activity. Many feature extraction techniques from such time-series signals exist in the literature. Time and frequency domain-based feature extraction is a widely used technique for sensor-based human activity classification. To train deep learning models, one needs features to be extracted from the signal. Though time and frequency domain feature extraction techniques are very efficient, the selection of the time and frequency domain features may have a significant impact on the overall classification accuracy. Alternatively, energy-based generative models eliminate the need for a feature extraction layer in the learning pipeline. Deep Belief Networks are alternatives to deep learning models eliminating the need for time and frequency-based feature extraction for sensor-based human activity classification: Restricted Boltzmann Machines (RBM) are the building blocks of Deep Belief Networks. RBMs are energy-based probabilistic graphical models which factorize the probability distribution of a random variable over a binary probability distribution. The visible layer of RBMs represents the real-valued random variable and the hidden layer represents the corresponding binary valued probability distribution. Conditional Restricted Boltzmann Machine (CRBM) is an extension to RBMs and is strong in capturing temporal dependency information encoded in time-series signals. They can be used in the classification of sensor-based human activities. The capacity of CRBM by factorizing a real-valued random variable probability distribution over a binary valued probability distribution eliminates the need for feature extraction from the signal by applying certain feature extraction techniques. This work shows how CRBM is trained to learn signal features. Once trained the signal is generated and reconstructed by the trained model. Along with CRBM, the results of other generative models RBM, GAN, WGAN-GP, and predictive model LSTM are also presented. To compare the performance of the models, similarity metrics are used as a performance criterion to show the performance of the generative models in generating the signals closest to the real signals. Euclidean, Canberra, and Dynamic Time Warping (DTW) distances are used as performance criteria. The results indicate that CRBM outperforms GAN, WGAN-GP, and RBM generative models in generating the signal closest to the original signal. LSTM performs close to CRBM. The capacity of the CRBM in generating signals closest to the original signal indicates that CRBM can learn features from the signal and can also be used in supervised classification.
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ÖgeYabancı dil öğrenimi için otomatik gramer egzersizi üretimi üzerine kullanıcı algılarının değerlendirilmesi(Lisansüstü Eğitim Enstitüsü, 2022-12-28) Bektaş, Fatih ; Eryiğit, Gülşen ; 504181514 ; Bilgisayar MühendisligiMorfolojik olarak zengin dillerin yapısı gereği, dilbilgisinin kelime düzeyinde gömülü olması nedeniyle öğrenciler için dil öğrenimi ve öğretmenler için dil alıştırmaları oluşturma süreci oldukça zor hale gelebilmektedir. Kelimelerin, çoklu çekimlemeler ve türetmeler nedeniyle karmaşıklıklıkları artabilmektedir. Yabancı dil öğrenicileri için bu dillerde, bir kelimenin başsözcüğünü bulmak ve sözlükten aramak bile zorlu bir görev olabilmektedir. Bu da başlangıç seviyelerindeki öğreniciler için fazla sayıda alıştırmaya maruz kalarak dilin morfolojik yapısını öğrenme gerekliliğini ortaya çıkarmaktadır. Dil eğitimi programlarında kullanılan ders kitaplarındaki sınırlı sayıdaki alıştırmalar yeterli olmamaktadır. Bu noktada dinamik olarak üretilebilecek alıştırmaların önemi açığa çıkmaktadır. Bu doğrultuda morfolojik olarak zengin diller için sonlu durum dönüştürücüsü tabanlı morfolojik çözümleyiciler ve morfolojik üreticiler önemli bir çözüm imkanı sunmaktadır. Teknolojinin gelişmesiyle birlikte eğitim ortamlarında artan oranda dijital teknolojilerden destek alınmaktadır. Akıllı telefonların giderek daha fazla öğrenme amacıyla kullanılması ile birlikte, mobil uygulamaların eğitimdeki konumu büyümektedir. Bu doğrultuda mobil destekli dil öğrenimi konsepti giderek popüler hale gelmektedir. Ayrıca mobil destekli dil öğreniminde oyunlaştırma yaklaşımları, dil öğrenme uygulamalarının etkililiğini artırmaktadır. Bu çalışmada, karmaşık morfoloji öğrenimi için İTÜ'de geliştirilen sonlu durum dönüştürücüsü tabanlı bir oyunlaştırma yaklaşımının, yabancı dil olarak Türkçe öğrenenler üzerindeki motivasyonel ve davranışsal sonuçları araştırılmaktadır. Karmaşık morfoloji öğrenimi için önerilen oyunlaştırma yaklaşımına yönelik öğrencilerin algıları ölçülerek nicel ve nitel analizler yapılmıştır. Analizler sonucu ortaya çıkan yeni isterler neticesinde, söz konusu mobil uygulama farklı arayüz dillerini ve Türkçeden farklı dilleri destekleyebilecek bir yapıya kavuşturulmuştur. Test amaçlı olarak Fransızca dili için gerçekleme yapılmıştır. Ayrıca uygulamada sunulan alıştırmaların, gerçek hayattan alınan bağlamlar ile genişletilmesi doğrultusunda çalışmalar yürütülmüştür. Öğrenci algılarının ölçülmesi adına yürütülen deney 3 haftalık bir kapsamda sürdürülmüştür. Deneyin katılımcıları, A1 seviyesi Türkçe dil eğitimi sınıfında bulunan yabancı öğrencilerdir. Uygulama, dil eğitiminde yardımcı bir araç olarak kullanılmıştır. Katılımcıların deney süresi içerisinde, müfredatlarına paralel olarak uygulama içerisindeki bazı oyunları oynamaları istenmiştir. Deney süreci içerisinde günlük uygulaması yapılmış, deney sonunda anket ve yarı yapılandırılmış odak grup görüşmesi uygulanmıştır. Sonuçlar, katılımcıların büyük çoğunluğunun uygulama hakkında olumlu bir algıya sahip olduklarını göstermiştir. Öğrencilerin önceki mobil destekli dil öğrenimi deneyimlerine kıyasla önerilen yaklaşım, öğrencilerin müfredata uygun olan dilbilgisi alıştırmaları ihtiyaçlarını karşıladığı için beğenilmiştir. Bulgular, öğrencilerin bu yaklaşımdan sınıf ortamlarında ek materyal olarak yararlanabileceğini göstermektedir. Kullanım istatistikleri ayrıca, oyun ögelerinin öğrenciler arasında rekabeti beslediğini ve statik alıştırmalardan farklı olarak onları birbirinden farklı, dinamik olarak üretilmiş içeriklerle alıştırmaları tekrar etmeye yönlendirdiğini ortaya koymuştur. Türkçe için diğer mobil destekli dil öğrenimi uygulamaları ile deneyim sahibi olan katılımcıların görüşleri ışığında, önerilen yaklaşımın morfolojik olarak zengin diller için dilbilgisinin açık alıştırmalar aracılığı ile öğretimi alanında önemli bir boşluğu doldurduğu görülmektedir.