Learning Task Research Articles

Multi-scale task-aware structure graph modeling for few-shot image recognition

The Few-shot image recognition attempts to recognize images from a novel class with only a limited number of labeled training images, which is a few-shot learning (FSL) task. FSL is very challenging. Limited labeled training samples cannot adequately represent the distribution of classes, and the base and novel classes in the training and testing stages do not intersect and have different distributions, leading to a domain shift problem in generalizing the learned model to the novel class dataset. In this paper, we propose multi-scale task-aware structure graph modeling for few-shot image recognition. We train a meta-filter learner to generate task-aware local structure filters for each scale and adaptively capture the local structures at each scale. Moreover, we introduce a novel multi-scale graph attention network (MGAT) module to model the multi-scale local structures of an image, fully exploring the correlations between different local structures at all scales of the image. Finally, we leverage the attention mechanism of graph attention network to achieve information aggregation and propagation, aiming to obtain more representative and discriminative local structure features that integrate both local and global information. We conducted comprehensive experiments on four benchmark datasets widely adopted in FSL tasks. The experimental results demonstrate that the MTSGM obtains state-of-the-art performance, which validates the effectiveness of MTSGM.

Motor learning alters vision, but vision does not alter motor learning.

During visuomotor learning, improvements in motor performance accompany changes in how people use vision. However, the dependencies between altered visual reliance and improvements in motor skill is unclear. The present studies used an online sequence learning task to quantify how changing the availability of visual information affected motor skill learning (study 1) and how changing motor skill affected visual reliance (study 2). Participants used their keyboard to respond to targets falling vertically down a game screen. In study 1 (n = 49), the availability of visual information was altered by manipulating where the targets were visible on the screen. Three experimental groups practiced the task during full or limited vision conditions (when the targets were only visible in specific areas). We hypothesized that limiting visual information would reduce motor learning (i.e., the rate of improvement during training trial blocks). Instead, although participants performed worse during limited vision trials (P < 0.001), there was no difference in learning rate (P = 0.87). In study 2 (n = 119), all participants practiced the task with full vision and their visual reliance (i.e., their performance change between full and limited vision conditions) was quantified before and after training. We hypothesized that with motor learning, visual reliance on future targets would increase, whereas visual reliance on the current targets would decrease. The results of study 2 partially support our hypotheses with visual reliance decreasing for all visual areas (P < 0.001). Together, the results suggest changing motor skill alters how people use vision, but changing visual availability does not affect motor learning.NEW & NOTEWORTHY Previous research has established how people use visual information changes with motor learning. However, the dependencies of these two processes on each other are unclear. We find that limiting the availability of visual information degrades motor performance but not motor learning. We also find that motor learning reduces the impact of limiting the availability of visual information on motor performance. Together, these results suggest that how people use visual information depends on their motor skill.

Flipbook food chain in an ecosystem to improve learning outcomes in elementary school

The lack of ability of teachers to develop technology-based learning media and low interaction with learning tasks negatively affect students' learning outcomes. The study aims to determine the feasibility and effectiveness of a web-based Fiborampae (Flip Book Food Chain in Ecosystems) course on IPAS material. The research method used is the RnD model Borg and Gall which consists of several stages, namely: potential and problems; data collection; product design; design validation; design revision; product trials; product revision; trial use in which the data collection methods include questionnaires and test. Based on the validation team's assessment, Fiborampae media is included in the most appropriate category, which is also a finding in this research. The evaluation results for the content were 93%, and the media factor was 90.8%. The results of the N-Gain test on the pre-and post-test values showed a mean difference of 0.739 under a moderate criterion and an effectiveness value of 73.95 under a highly effective definition. Meanwhile, the reference category reached a value of 0.425 under moderate criteria and had an efficiency of 42.35 under the low-efficiency definition. The interpretation of the data shows that classrooms using Fiborampae media achieve higher learning outcomes with more effective standards. The implications of this research are expected to apply this medium to simpler devices with a simple and attractive appearance that can be used in various teaching situations and support character development within the new curriculum.

Development of an Operating System Supporting Intelligent Predictions and Recommendations

This Study discusses the development of an intelligent operating system feature that supports smart prediction and recommendations using artificial intelligence (AI) capabilities within the Linux operating system. The study aims to integrate AI-driven features into Linux to enhance user productivity and efficiency by providing relevant application recommendations based on user behavior patterns. The implementation involves data collection of application usage, training machine learning models for application recommendations, and integrating these features into the Linux environment. The project utilizes Python for scripting, employing libraries such as psutil, pandas, scikit-learn, and joblib for data handling and machine learning tasks. The results demonstrate successful implementation of the AI-driven recommendation system, enhancing user interaction and productivity within the Linux operating system

Spontaneous instrumental approach-avoidance learning in social contexts in autism

BackgroundIndividuals with Autism Spectrum Condition (ASC) are characterized by atypicalities in social interactions, compared to Typically Developing individuals (TD). The social motivation theory posits that these difficulties stem from diminished anticipation, reception, and/or learning from social rewards. Although learning from socioemotional outcomes is core to the theory, studies to date have been sparse and inconsistent. This possibly arises from a combination of theoretical, methodological and sample-related issues. Here, we assessed participants’ ability to develop a spontaneous preference for actions that lead to desirable socioemotional outcomes (approaching/avoiding of happy/angry individuals, respectively), in an ecologically valid social scenario. We expected that learning abilities would be impaired in ASC individuals, particularly in response to affiliative social feedback.MethodWe ran an online social reinforcement learning task, on two large online cohorts with (n = 274) and without (n = 290) ASC, matched for gender, age and education. Participants had to indicate where they would sit in a waiting room. Each seat was associated with different probabilities of approaching/avoiding emotional individuals. Importantly, the task was implicit, as participants were not instructed to learn, and emotional expressions were never mentioned. We applied both categorical analyses contrasting the ASC and TD groups and dimensional factor analysis on affective questionnaires.ResultsContrary to our hypothesis, participants showed spontaneous learning from socioemotional outcomes, regardless of their diagnostic group. Yet, when accounting for dimensional variations in autistic traits, as well as depression and anxiety, two main findings emerged among females who failed to develop explicit learning strategies: (1) autism severity in ASC correlated with reduced learning to approach happy individuals; (2) anxiety-depression severity across both ASC and TD participants correlated with reduced learning to approach/avoid happy/angry individuals, respectively.ConclusionsImplicit spontaneous learning from socioemotional outcomes is not generally impaired in autism but may be specifically associated with autism severity in females with ASC, when they do not have an explicit strategy for adapting to their social environment. Clinical diagnosis and intervention ought to take into account individual differences in their full complexity, including the presence of co-morbid anxiety and depression, when dealing with social atypicalities in autism.

Cognitive ease at a cost: LLMs reduce mental effort but compromise depth in student scientific inquiry

This study explores the cognitive load and learning outcomes associated with using large language models (LLMs) versus traditional search engines for information gathering during learning. A total of 91 university students were randomly assigned to either use ChatGPT3.5 or Google to research the socio-scientific issue of nanoparticles in sunscreen to derive valid recommendations and justifications. The study aimed to investigate potential differences in cognitive load, as well as the quality and homogeneity of the students' recommendations and justifications. Results indicated that students using LLMs experienced significantly lower cognitive load. However, despite this reduction, these students demonstrated lower-quality reasoning and argumentation in their final recommendations compared to those who used traditional search engines. Further, the homogeneity of the recommendations and justifications did not differ significantly between the two groups, suggesting that LLMs did not restrict the diversity of students’ perspectives. These findings highlight the nuanced implications of digital tools on learning, suggesting that while LLMs can decrease the cognitive burden associated with information gathering during a learning task, they may not promote deeper engagement with content necessary for high-quality learning per se.

Unveiling individuality in the early phase of motor learning: a machine learning approach for analysing weightlifting technique in novices.

Despite the growing body of evidence highlighting the individuality in movement techniques, predominant models of motor learning, particularly during the acquisition phase, continue to emphasise generalised, person-independent approaches. Biomechanical studies, coupled with machine learning approaches, have demonstrated the uniqueness of movement techniques exhibited by individuals. However, this evidence predominantly pertains to already stabilised movement techniques, particularly evident in cyclic daily activities such as walking, running, or cycling, as well as in expert-level sports movements. This study aims to evaluate the hypothesis of individuality in whole-body movements necessitating intricate coordination and strength among novice participants at the very beginning of an acquisition phase. In a within-subject design, sixteen highly active male participants (mean age: 23.1 ± 2.1 years), all absolute novices in the learning task (i.e., power snatch of Olympic weightlifting), participated in randomised snatch learning bouts. These bouts comprised 36 trials across various motor learning models: differential learning contextual interference (serial, sCIL; and blocked, bCIL), and repetitive learning. Kinematic and kinetic data were collected from three standardised snatch trials performed following each motor learning model bout. The time-continuous data were input to a linear Support Vector Machine (SVM). We conducted analyses on two classification tasks: participant and motor learning model. The Support Vector Machine classification revealed a notably superior participant classification compared to the motor learning model classification, with an averaged prediction accuracy of 78% (in average ≈35 out of 45 test trials across the folds) versus 27.3% (in average ≈9 out of 36 test trials across the folds). In specific fold and input combinations, accuracies of 91% versus 38% were respectively achieved. Methodically, the crucial role of selecting appropriate data pre-processing methods and identifying the optimal combinations of SVM data inputs is discussed in the context of future research. Our findings provide initial support for a dominance of individuality over motor learning models in movement techniques during the early phase of acquisition in Olympic weightlifting power snatch.

Hybrid modeling for improved extrapolation and transfer learning in the chemical processing industry

In the Chemical Processing Industry, shifts in market demand often require the implementation of new operational modes that balance economic advantages with the need to meet quality and sustainability targets. Hybrid modeling can support these process changes by combining physics-based and data-driven modeling principles to improve prediction accuracy and interpretation. In this work, the use of hybrid modeling for improved extrapolation and transfer learning activities is examined in the context of simulated biodiesel production. We study and compare different configurations of hybrid modeling, including the parallel and serial structures. We also compare hybrid modeling approaches with physics-based and data-driven models, and find that hybrid modeling consistently outperforms these benchmarks in both extrapolation and transfer learning tasks. Hybrid modeling also requires fewer samples than other benchmarks for the transfer learning task. These results suggest that hybrid modeling is an effective approach for supporting decision-making and optimizing process changes in the CPI.

Distinct alterations in probabilistic reversal learning across at-risk mental state, first episode psychosis and persistent schizophrenia

We used a probabilistic reversal learning task to examine prediction error-driven belief updating in three clinical groups with psychosis or psychosis-like symptoms. Study 1 compared people with at-risk mental state and first episode psychosis (FEP) to matched controls. Study 2 compared people diagnosed with treatment-resistant schizophrenia (TRS) to matched controls. The design replicated our previous work showing ketamine-related perturbations in how meta-level confidence maintained behavioural policy. We applied the same computational modelling analysis here, in order to compare the pharmacological model to three groups at different stages of psychosis. Accuracy was reduced in FEP, reflecting increased tendencies to shift strategy following probabilistic errors. The TRS group also showed a greater tendency to shift choice strategies though accuracy levels were not significantly reduced. Applying the previously-used computational modelling approach, we observed that only the TRS group showed altered confidence-based modulation of responding, previously observed under ketamine administration. Overall, our behavioural findings demonstrated resemblance between clinical groups (FEP and TRS) and ketamine in terms of a reduction in stabilisation of responding in a noisy environment. The computational analysis suggested that TRS, but not FEP, replicates ketamine effects but we consider the computational findings preliminary given limitations in performance of the model.

The New Mixed Teaching Exploration of Low-Voltage Electrician Training Course under the Background of New Engineering

In the context of New Engineering, the exploration of a novel blended teaching approach for low-voltage electrician training courses aims to address the challenges of modern engineering education by integrating New Engineering concepts and modern teaching technologies to achieve comprehensive innovation in course content and teaching methods. This strategy leverages the advantages of combining online and offline learning to provide a diverse and personalized learning experience, thereby enhancing students' knowledge and practical skills. Online course resources, including interactive courseware, virtual experiments, and simulations, enrich the learning content and increase students' interest and engagement. Offline classes focus on interactivity and practical application through discussions, case studies, and project collaborations, helping students deepen their understanding and apply their knowledge. Flexible scheduling allows students to learn at their own pace, improving learning efficiency and effectiveness. Personalized learning path design, through preliminary surveys and assessments, helps students set individualized learning goals. Utilizing big data analysis and intelligent recommendation algorithms, the system provides the most suitable learning resources. Students can autonomously adjust their learning paths, fostering self-directed learning, while teachers offer targeted guidance and support to ensure the successful completion of learning tasks. Interactive and collaborative learning environments are strengthened through online and offline interaction mechanisms, group collaborative learning, and the use of various interactive tools, enhancing classroom interaction and participation. The establishment of online learning communities promotes resource sharing and experience exchange, creating a positive learning atmosphere. Assessment and feedback employ formative and practical assessments through online tests, assignments, and project work, comprehensively reflecting students' learning progress and outcomes. Multi-dimensional feedback mechanisms and data-driven evaluation provide detailed learning analysis and improvement suggestions for both teachers and students, enhancing teaching effectiveness.

Negative order sobolev cubatures: preconditioners of partial differential equation learning tasks circumventing numerical stiffness

We present a variational approach aimed at enhancing the training of physics-informed neural networks (PINNs) and more general surrogate models for learning partial differential equations (PDE). In particular, we extend our formerly introduced notion of Sobolev cubatures to negative orders, enabling the approximation of negative order Sobolev norms. We mathematically prove the effect of negative order Sobolev cubatures in improving the condition number of discrete PDE learning problems, providing balancing scalars that mitigate numerical stiffness issues caused by loss imbalances. Additionally, we consider polynomial surrogate models (PSMs), which maintain the flexibility of PINN formulations while preserving the convexity structure of the PDE operators. The combination of negative order Sobolev cubatures and PSMs delivers well-conditioned discrete optimization problems, solvable via an exponentially fast convergent gradient descent for λ-convex losses. Our theoretical contributions are supported by numerical experiments, addressing linear and non-linear, forward and inverse PDE problems. These experiments show that the Sobolev cubature-based PSMs emerge as the superior state-of-the-art PINN technique.

Learning-to-learn as a metacognitive correlate of functional outcomes after stroke: a cohort study.

Meta-learning is a metacognitive function for successful, efficient learning in various tasks. While it is possible that meta-learning is linked to functional recovery in stroke, it has not been investigated in previous clinical research on metacognition. Examine if individual meta-learning ability is associated with functional outcomes. Cohort study. Rehabilitation ward in Fujita Health University Hospital. Twenty-nine hemiparetic people after stroke. The study measured individual sensorimotor adaptation rate, meta-learning (acceleration of adaptation through training), and Functional Independence Measure (FIM) motor effectiveness, an index of functional outcome measuring improvement in proficiency of activity of daily living (ADL). Participants performed visuomotor adaptation training sessions with their less-affected arm. They made arm-reaching movements to hit a target with cursor feedback, which was occasionally rotated with regard to their hand positions, requiring them to change the movement direction accordingly. Initial adaptation rate and meta-learning were quantified from pre- and post-training tests. The relationship between these indices of adaptation ability and FIM motor effectiveness was examined by multiple linear regression analyses. One participant was excluded before data collection in the motor task. In the remaining 28 individuals, the regression analyses revealed that FIM motor effectiveness positively correlated with meta-learning (µ=0.90, P=0.008), which was attenuated by age (µ=-0.015, P=0.005), but not with initial adaptation rate (P=0.08). Control analyses suggested that this observed association between FIM motor effectiveness and meta-learning was not mediated by patients' demographics or stroke characteristics. This study demonstrates that those who can accelerate adaptation through training are likely to improve ADL, suggesting that meta-learning may be linked with functional outcomes in some stroke individuals. Meta-learning may enable the brain to keep (re-)learning motor skills when motor functions change abruptly due to stroke and neural recovery, thereby associated with improvement in ADL. Meta-learning is part of metacognitive functions that is positively associated with functional outcomes.

An Improved Binary Crayfish Optimization Algorithm for Handling Feature Selection Task in Supervised Classification

Feature selection (FS) is a crucial phase in data mining (DM) and machine learning (ML) tasks, aimed at removing uncorrelated and redundant attributes to enhance classification accuracy. This study introduces an improved binary crayfish optimization algorithm (IBCOA) designed to tackle the FS problem. The IBCOA integrates a local search strategy and a periodic mode boundary handling technique, significantly improving its ability to search and exploit the feature space. By doing so, the IBCOA effectively reduces dimensionality, while improving classification accuracy. The algorithm’s performance was evaluated using support vector machine (SVM) and k-nearest neighbor (k-NN) classifiers on eighteen multi-scale benchmark datasets. The findings showed that the IBCOA performed better than nine recent binary optimizers, attaining 100% accuracy and decreasing the feature set size by as much as 0.8. Statistical evidence supports that the proposed IBCOA is highly competitive according to the Wilcoxon rank sum test (alpha = 0.05). This study underscores the IBCOA’s potential for enhancing FS processes, providing a robust solution for high-dimensional data challenges.

Creative Problem Solving and Communication in Entrepreneurship

This abstract introduces a plug and play set of materials designed for teachers to use as flipped learning tasks in the context of proposal writing and persuasive presentations. These materials were developed based on interview videos featuring young local entrepreneurs, who provided valuable insights and perspectives into the importance of research, creative problem solving, communication and language skills. PolyU students in discipline-specific subjects engage in project-based learning, where they write proposals and deliver persuasive presentations based on their unique understanding and ideas within their respective disciplines. Given the constraints of a tight 13-week schedule, flipped discovery and consolidation materials based on the interview videos have been developed, covering a range of topics related to research, proposal writing, and presentation skills. This submission centers around two plug-and-play sets: Creative Problem Solving and Communication, which can be seamlessly integrated into existing teaching and learning materials as flipped warmer and consolidation activities. Students have expressed in focus group interviews how these materials have made their learning experience more authentic and transferable. By incorporating relatable real-world examples and practical insights, these plug-and-play sets offer teachers a convenient and effective way to introduce engaging warm-up activities into their existing materials. Students are guided to reflect on their research process, creative problem-solving skills and communication skills which are essential for successfully communicating their discipline knowledge and ideas in proposal writing and persuasive presentations.

A novel artificial intelligence model for diagnosing Acanthamoeba keratitis through confocal microscopy

PurposeTo develop an artificial intelligence (AI) model to diagnose Acanthamoeba keratitis (AK) based on in vivo confocal microscopy (IVCM) images extracted from the Heidelberg Retinal Tomograph 3 (HRT 3). MethodsThis retrospective cohort study utilized HRT 3 IVCM images from patients who had received a culture-confirmed diagnosis of AK between 2013 and 2021 at Massachusetts Eye and Ear. Two cornea specialists independently labeled the images as AK or nonspecific finding (NSF) in a blind manner. Deep learning tasks were then conducted through Python and TensorFlow. Distinguishing between AK and NSF was designed as the task and completed through a devised convolutional neural network. ResultsA dataset of 3312 confocal images from 17 patients with a culture-confirmed diagnosis of AK was used in this study. The inter-rater agreement for identifying the presence or absence of AK in IVCM images was 84 %, corresponding to a total of 2782 images on which both observers agreed and which were included in the model. 1242 and 1265 images of AK and NSF, respectively, were utilized in the training and validation sets, and 173 and 102 images of AK and NSF, respectively, were utilized in the evaluation set. Our model had an accuracy, sensitivity, and specificity of 76 % each, and a precision of 78 %. ConclusionsWe developed an HRT-based IVCM AI model for AK diagnosis utilizing culture-confirmed cases of AK. We achieved good accuracy in diagnosing AK and our model holds significant promise in the clinical application of AI in improving early AK diagnosis.

The pivotal role of clinical tutorial teaching in knowledge construction.

Students' new knowledge is gradually built up in the context of the task for which it is required and consolidated by applying it to clinical cases. As students see more and more clinical cases the knowledge emerges from an associative mesh of different levels of understanding. During tutorial clinical teaching, residents should be gradually exposed to an increasing range of real-world learning tasks and increasing levels of complexity. This exposure allows them to gradually develop shortcuts in the retrieval of their knowledge. This commentary provides a rationale for the construction of knowledge and the pivotal role that clinical tutorial teaching plays in this task.

Towards Collaborative Edge Intelligence: Blockchain-Based Data Valuation and Scheduling for Improved Quality of Service

Collaborative edge intelligence, a distributed computing paradigm, refers to a system where multiple edge devices work together to process data and perform distributed machine learning (DML) tasks locally. Decentralized Internet of Things (IoT) devices share knowledge and resources to improve the quality of service (QoS) of the system with reduced reliance on centralized cloud infrastructure. However, the paradigm is vulnerable to free-riding attacks, where some devices benefit from the collective intelligence without contributing their fair share, potentially disincentivizing collaboration and undermining the system’s effectiveness. Moreover, data collected from heterogeneous IoT devices may contain biased information that decreases the prediction accuracy of DML models. To address these challenges, we propose a novel incentive mechanism that relies on time-dependent blockchain records and multi-access edge computing (MEC). We formulate the QoS problem as an unbounded multiple knapsack problem at the network edge. Furthermore, a decentralized valuation protocol is introduced atop blockchain to incentivize contributors and disincentivize free-riders. To improve model prediction accuracy within latency requirements, a data scheduling algorithm is given based on a curriculum learning framework. Based on our computer simulations using heterogeneous datasets, we identify two critical factors for enhancing the QoS in collaborative edge intelligence systems: (1) mitigating the impact of information loss and free-riders via decentralized data valuation and (2) optimizing the marginal utility of individual data samples by adaptive data scheduling.

The Role of Mathematics in Artificial Intelligence and Machine Learning

Mathematics serves as the foundational backbone of artificial intelligence (AI) and machine learning (ML), providing the essential tools and frameworks for developing sophisticated algorithms and models. the pivotal role of various mathematical disciplines, including linear algebra, calculus, probability theory, and optimization, in advancing AI and ML technologies. We begin by examining how linear algebra facilitates the manipulation and transformation of high-dimensional data, which is crucial for techniques such as principal component analysis (PCA) and singular value decomposition (SVD). Next, we delve into the applications of calculus in training neural networks through gradient-based optimization methods, highlighting the importance of differentiation and integration in backpropagation and loss function minimization. the role of probability theory in handling uncertainty and making predictions, emphasizing its application in Bayesian networks, Markov decision processes, and probabilistic graphical models. Additionally, we discuss optimization techniques, both convex and non-convex, that are fundamental to finding optimal solutions in machine learning tasks, including support vector machines (SVMs) and deep learning architectures.

Automatic Quality Assessment of Pork Belly via Deep Learning and Ultrasound Imaging.

Pork belly, prized for its unique flavor and texture, is often overlooked in breeding programs that prioritize lean meat production. The quality of pork belly is determined by the number and distribution of muscle and fat layers. This study aimed to assess the number of pork belly layers using deep learning techniques. Initially, semantic segmentation was considered, but the intersection over union (IoU) scores for the segmented parts were below 70%, which is insufficient for practical application. Consequently, the focus shifted to image classification methods. Based on the number of fat and muscle layers, a dataset was categorized into three groups: three layers (n = 1811), five layers (n = 1294), and seven layers (n = 879). Drawing upon established model architectures, the initial model was refined for the task of learning and predicting layer traits from B-ultrasound images of pork belly. After a thorough evaluation of various performance metrics, the ResNet18 model emerged as the most effective, achieving a remarkable training set accuracy of 99.99% and a validation set accuracy of 96.22%, with corresponding loss values of 0.1478 and 0.1976. The robustness of the model was confirmed through three interpretable analysis methods, including grad-CAM, ensuring its reliability. Furthermore, the model was successfully deployed in a local setting to process B-ultrasound video frames in real time, consistently identifying the pork belly layer count with a confidence level exceeding 70%. By employing a scoring system with 100 points as the threshold, the number of pork belly layers in vivo was categorized into superior and inferior grades. This innovative system offers immediate decision-making support for breeding determinations and presents a highly efficient and precise method for assessment of pork belly layers.

Application of Multimedia Technology in Teaching English in Colleges and Universities

Multimedia plays a crucial role in English teaching by enhancing engagement, providing diverse learning experiences, and catering to different learning styles. English teaching through multimedia, while beneficial, presents challenges. Unequal access to technology and the digital divide can hinder some students' participation. Ensuring digital literacy and quality content selection is crucial to effective use. This paper proposed the Hidden Markov Model for English Teaching (HMM-ET) to improve the performance of college and university students. The proposed HMM-ET model computes the Markov chain of English teaching through multimedia technology. With the implementation of multimedia technology, the HMM model estimates the performance of students in colleges and universities. Through the estimation of HMM-ET the classification of students' performance in English learning is computed with the machine learning model. The performance of the students is examined comparatively with the conventional Support Vector Machine (SVM) and Random Forest. Through analysis of a dataset comprising observation sequences reflecting English learning tasks, HMM-ET consistently outperforms SVM and Random Forest, achieving an average accuracy of 96%, while SVM and Random Forest attain accuracies of 90% and 88% respectively.