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Mobile learning and gamification are becoming increasingly popular, especially in education. Numerous researches suggest that mobile learning apps with gamification can improve student learning. However, there are fewer studies that analyze the gamification effects and usability of mobile apps for middle-aged women. The aim of this study is to propose a new mobile learning app with gamification aspects for the use of middle-aged women in memorizing Arabic words. In the preliminary study, pre and post-tests were conducted to determine the effect of the use of the gamification application on 61 middle-aged women using Kahoot! game. The results from the Kahoot! game shows that participants enjoy learning and can memorize Arabic words effectively, indicating that gamification can motivate learning among middle-aged women. Then the usability of a new mobile app prototype containing learning notes and quizzes for user self-learning purposes was evaluated using three (3) criteria namely effectiveness, efficiency, and satisfaction. The considered indicators to measure the application's effectiveness are (a) tapping behaviour (number of taps used to perform a specific task), (b) number of attempts (how many times users tried to complete the task successfully), and (c) quiz score (score obtained by user after self-learning using the mobile app). Two variables were used to measure the efficiency of the application: the duration of task completion and the number of requests for help. In order to evaluate the satisfaction, a questionnaire was used to determine how respondents rated the mobile app's usability. The usability evaluation results show that the proposed mobile app was effective as users could utilize all the modules provided and successfully answer the quiz questions.

Speech enhancement algorithms are applied in multiple levels of enhancement to improve the quality of speech signals under noisy environments known as multi-channel speech enhancement (MCSE) systems. Numerous existing algorithms are used to filter noise in speech enhancement systems, which are typically employed as a pre-processor to reduce noise and improve speech quality. They may, however, be limited in performing well under low signal-to-noise ratio (SNR) situations. The speech devices are exposed to all kinds of environmental noises which may go up to a high-level frequency of noises. The objective of this research is to conduct a noise reduction experiment for a multi-channel speech enhancement (MCSE) system in stationary and non-stationary environmental noisy situations with varying speech signal SNR levels. The experiments examined the performance of the existing and the proposed MCSE systems for environmental noises in filtering low to high SNRs environmental noises (-10 dB to 20 dB). The experiments were conducted using the AURORA and LibriSpeech datasets, which consist of different types of environmental noises. The existing MCSE (BAV-MCSE) makes use of beamforming, adaptive noise reduction and voice activity detection algorithms (BAV) to filter the noises from speech signals. The proposed MCSE (DWT-CNN-MCSE) system was developed based on discrete wavelet transform (DWT) preprocessing and convolution neural network (CNN) for denoising the input noisy speech signals to improve the performance accuracy. The performance of the existing BAV-MCSE and the proposed DWT-CNN-MCSE were measured using spectrogram analysis and word recognition rate (WRR). It was identified that the existing BAV-MCSE reported the highest WRR at 93.77% for a high SNR (at 20 dB) and 5.64% on average for a low SNR (at -10 dB) for different noises. The proposed DWT-CNN-MCSE system has proven to perform well at a low SNR with WRR of 70.55% and the highest improvement (64.91% WRR) at -10 dB SNR. [ABSTRACT FROM AUTHOR]

Having improved emotional (affective) state may have several benefits on learners, such as promoting higher cognitive flexibility and opens the learner to discovery of new ideas and possibilities. On other side, negative emotional states like boredom and frustration have been linked with less use of self-regulation and cognitive strategies for learning as well as increases in disengaged and disturbing behavior during learning. In the area of computerised learning, several researchers strongly agree that intelligent tutoring systems (ITSs) would significantly improve its performance if it can adapt to the affective state (emotional state) of the learners. This idea has spawned an important trend in the development of ITSs, which are systems with the ability to regulate a learner's adverse emotions. In the present study, we discuss the existing studies that have implemented different emotion regulation strategies such as coping strategies and implementation of these strategies in the domain of intelligent tutoring system (ITS). The results of the review show that applying emotion regulation strategies during computerised learning may produce more optimistic emotions as well as better learning gain.

Automatic speech recognition (ASR) for dysarthric speakers is one of the most challenging research areas. The lack of corpus for dysarthric speakers makes it even more difficult. The speaker adaptation (SA) is an alternative solution to overcome the lack of dysarthric speech and enhance the performance of ASR. This paper introduces the Severity-based adaptation, using small amount of speech data, in which data from all participants in a given severity type will use for adaptation of that type. The adaptation is performed for two types of acoustic models, which are the Controlled Acoustic Model (CAM) developed using rich phonetic corpus, and Dysarthric Acoustic Model (DAM) that includes speech collected from dysarthric speakers suffering from variety level of severity. This paper compares two adaptation techniques for building ASR systems for dysarthric speakers, which are Maximum Likelihood Linear Regression (MLLR) and Constrained Maximum Likelihood Linear Regression (CMLLR). The result shows that the Word Recognition Accuracy (WRA) for the CAM outperformed DAM for both the Speaker Independent (SI) and Speaker Adaptation (SA). On the other hand, it was found that MLLR is outperformed the CMLLR for both Controlled Speaker Adaptation (CSA) and Dysarthric Speaker Adaptation (DSA). [ABSTRACT FROM AUTHOR]