The study explored a new technique of phonetic tone training, which may have a positive impact on second language learning and tone training.

In this study, I explored the impact of Generative AI on learning efficacy in academic reading materials using experimental methods. College-educated participants engaged in three cycles of reading and writing tasks. After each cycle, they responded to comprehension questions related to the material. After adjusting for background knowledge and demographic factors, complete reliance on AI for writing tasks led to a 25.1% reduction in accuracy. In contrast, AI-assisted reading resulted in a 12% decline. Interestingly, using AI for summarization significantly improved both quality and output. Accuracy exhibited notable variance in the AI-assisted section. Further analysis revealed that individuals with a robust background in the reading topic and superior reading/writing skills benefitted the most. I conclude the research by discussing educational policy implications, emphasizing the need for educators to warn students about the dangers of over-dependence on AI and provide guidance on its optimal use in educational settings.

Social interactions and conversation skills separate the successful from the rest and the confident from the shy. For college students in particular, the ability to converse can be an outlet for the stress and anxiety experienced on a daily basis along with a foundation for all-important career skills. In light of this, we designed SpeakEasy: a chatbot with some degree of intelligence that provides feedback to the user on their ability to engage in free-form conversations with the chatbot. SpeakEasy attempts to help college students improve their communication skills by engaging in a seven-minute spoken conversation with the user, analyzing the user's responses with metrics designed based on previous psychology and linguistics research, and providing feedback to the user on how they can improve their conversational ability. To simulate natural conversation, SpeakEasy converses with the user on a wide assortment of topics that two people meeting for the first time might discuss: travel, sports, and entertainment. Unlike most other chatbots with the goal of improving conversation skills, SpeakEasy actually records the user speaking, transcribes the audio into tokens, and uses macros-e.g., sequences that calculate the pace of speech, determine if the user has an over-reliance on certain words, and identifies awkward transitions-to evaluate the quality of the conversation. Based on the evaluation, SpeakEasy provides elaborate feedback on how the user can improve their conversations. In turn, SpeakEasy updates its algorithms based on a series of questions that the user responds to regarding SpeakEasy's performance.

This study investigates game-based learning in the context of the educational game "Jo Wilder and the Capitol Case," focusing on predicting student performance using various machine learning models, including K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), and Random Forest. The research aims to identify the features most predictive of student performance and correct question answering. By leveraging gameplay data, we establish complete benchmarks for these models and explore the importance of applying proper data aggregation methods. By compressing all numeric data to min/max/mean/sum and categorical data to first, last, count, and nunique, we reduced the size of the original training data from 4.6 GB to 48 MB of preprocessed training data, maintaining high F1 scores and accuracy. Our findings suggest that proper preprocessing techniques can be vital in enhancing the performance of non-deep-learning-based models. The MLP model outperformed the current state-of-the-art French Touch model, achieving an F-1 score of 0.83 and an accuracy of 0.74, suggesting its suitability for this dataset. Future research should explore using larger datasets, other preprocessing techniques, more advanced deep learning techniques, and real-world applications to provide personalized learning recommendations to students based on their predicted performance. This paper contributes to the understanding of game-based learning and provides insights into optimizing educational game experiences for improved student outcomes and skill development.

This article describes the MyST corpus developed as part of the My Science Tutor project -- one of the largest collections of children's conversational speech comprising approximately 400 hours, spanning some 230K utterances across about 10.5K virtual tutor sessions by around 1.3K third, fourth and fifth grade students. 100K of all utterances have been transcribed thus far. The corpus is freely available (https://myst.cemantix.org) for non-commercial use using a creative commons license. It is also available for commercial use (https://boulderlearning.com/resources/myst-corpus/). To date, ten organizations have licensed the corpus for commercial use, and approximately 40 university and other not-for-profit research groups have downloaded the corpus. It is our hope that the corpus can be used to improve automatic speech recognition algorithms, build and evaluate conversational AI agents for education, and together help accelerate development of multimodal applications to improve children's excitement and learning about science, and help them learn remotely.

We present the latest version of the Spanish Resource Grammar (SRG). The new SRG uses the recent version of Freeling morphological analyzer and tagger and is accompanied by a manually verified treebank and a list of documented issues. We also present the grammar's coverage and overgeneration on a small portion of a learner corpus, an entirely new research line with respect to the SRG. The grammar can be used for linguistic research, such as for empirically driven development of syntactic theory, and in natural language processing applications such as computer-assisted language learning. Finally, as the treebanks grow, they can be used for training high-quality semantic parsers and other systems which may benefit from precise and detailed semantics.

With the advancing technology, the hardware gain of computers and the increase in the processing capacity of processors have facilitated the processing of instantaneous and real-time images. Face recognition processes are also studies in the field of image processing. Facial recognition processes are frequently used in security applications and commercial applications. Especially in the last 20 years, the high performances of artificial intelligence (AI) studies have contributed to the spread of these studies in many different fields. Education is one of them. The potential and advantages of using AI in education; can be grouped under three headings: student, teacher, and institution. One of the institutional studies may be the security of educational environments and the contribution of automation to education and training processes. From this point of view, deep learning methods, one of the sub-branches of AI, were used in this study. For object detection from images, a pioneering study has been designed and successfully implemented to keep records of students' entrance to the educational institution and to perform class attendance with images taken from the camera using image processing algorithms. The application of the study to real-life problems will be carried out in a school determined in the 2022-2023 academic year.

Recently, learning-based approaches show promising results in navigation tasks. However, the poor generalization capability and the simulation-reality gap prevent a wide range of applications. We consider the problem of improving the generalization of mobile robots and achieving sim-to-real transfer for navigation skills. To that end, we propose a cross-modal fusion method and a knowledge transfer framework for better generalization. This is realized by a teacher-student distillation architecture. The teacher learns a discriminative representation and the near-perfect policy in an ideal environment. By imitating the behavior and representation of the teacher, the student is able to align the features from noisy multi-modal input and reduce the influence of variations on navigation policy. We evaluate our method in simulated and real-world environments. Experiments show that our method outperforms the baselines by a large margin and achieves robust navigation performance with varying working conditions.

Large Language Models (LLMs) trained on massive corpora demonstrate impressive capabilities in a wide range of tasks. While there are ongoing efforts to adapt these models to languages beyond English, the attention given to their evaluation methodologies remains limited. Current multilingual benchmarks often rely on back translations or re-implementations of English tests, limiting their capacity to capture unique cultural and linguistic nuances. To bridge this gap for the Korean language, we introduce HAE-RAE Bench, a dataset curated to challenge models lacking Korean cultural and contextual depth. The dataset encompasses six downstream tasks across four domains: vocabulary, history, general knowledge, and reading comprehension. Contrary to traditional evaluation suites focused on token or sequence classification and specific mathematical or logical reasoning, HAE-RAE Bench emphasizes a model's aptitude for recalling Korean-specific knowledge and cultural contexts. Comparative analysis with prior Korean benchmarks indicates that the HAE-RAE Bench presents a greater challenge to non-native models, by disturbing abilities and knowledge learned from English being transferred.

Online continual learning aims to get closer to a live learning experience by learning directly on a stream of data with temporally shifting distribution and by storing a minimum amount of data from that stream. In this empirical evaluation, we evaluate various methods from the literature that tackle online continual learning. More specifically, we focus on the class-incremental setting in the context of image classification, where the learner must learn new classes incrementally from a stream of data. We compare these methods on the Split-CIFAR100 and Split-TinyImagenet benchmarks, and measure their average accuracy, forgetting, stability, and quality of the representations, to evaluate various aspects of the algorithm at the end but also during the whole training period. We find that most methods suffer from stability and underfitting issues. However, the learned representations are comparable to i.i.d. training under the same computational budget. No clear winner emerges from the results and basic experience replay, when properly tuned and implemented, is a very strong baseline. We release our modular and extensible codebase at https://github.com/AlbinSou/ocl_survey based on the avalanche framework to reproduce our results and encourage future research.