Context-based Model Research Articles

Examining the cognitive and affective changes in students through the implementation process of the physics curriculum based on an interdisciplinary context-based learning approach

This case study examines the cognitive and affective changes in students through the implementation process of the physics curriculum based on an interdisciplinary context-based learning approach. The study was conducted during the second semester of the 2017-2018 academic year with 33 students in one of the 10th grade classes of a high school in the Yüreğir district of the Adana province. A criterion sampling method of purposive sampling methods was employed in selecting the school and the class. In the intervention, the researchers used the physics curriculum they developed based on an interdisciplinary context-based learning approach. The plans and activities prepared according to the context-based REACT model were implemented for eight weeks and the data were collected using both quantitative and qualitative methods. At the end of the intervention, significant changes were observed in the students’ ability to relate their knowledge to daily life, interdisciplinary problem-solving skills, attitudes towards problem-solving in physics, and affective characteristics. During the process, students who encountered challenges in problem-solving, focusing on the lesson, working in groups, and relating information to real-life and different disciplines managed to overcome these challenges by increasing their individual efforts and communicating with their peers and the teacher. Because of students who remained passive, made noise, and frequently changed their seats during group work, the researchers encountered some challenges in instruction and time management. To increase classroom participation, they opted for updating the lesson plan, preparing additional examples, and increasing the number of visual materials. In this respect, teachers and other researchers are recommended to allocate more time to interdisciplinary context-based teaching activities, provide students with more real-life problem situations, diversify group work, and conduct group activities more frequently.

A context-based model to predict the intelligibility of sentences in non-stationary noises.

The context-based Extended Speech Transmission Index (cESTI) (van Schoonhoven et al., 2022, J. Acoust. Soc. Am. 151, 1404-1415) was successfully applied to predict the intelligibility of monosyllabic words with different degrees of context in interrupted noise. The current study aimed to use the same model for the prediction of sentence intelligibility in different types of non-stationary noise. The necessary context factors and transfer functions were based on values found in existing literature. The cESTI performed similar to or better than the original ESTI when noise had speech-like characteristics. We hypothesize that the remaining inaccuracies in model predictions can be attributed to the limits of the modelling approach with regard to mechanisms, such as modulation masking and informational masking.

Meningkatkan Literasi Numerasi Melalui Pembelajaran Berbasis Masalah Berkonteks Islam dengan Berbantuan Geogebra

This research aims to implement a context-based problem-solving learning model with Islamic context assisted by GeoGebra to enhance the numeracy literacy skills of students in the topic of matrix in Grade XI MIPA at MAN Barito Timur. The study employs a classroom action research approach with two cycles. Data is collected through pretests, post-tests, observations of learning implementation, and student worksheets. The results indicate that applying the learning model successfully improves students' numeracy literacy skills. In cycle 1, there was an increase from 5 students reaching the minimum passing grade (KKM) in the pretest to 9 students or 56% in the post-test. In cycle 2, 7 students achieved KKM in the pretest, which increased to 12 students in the post-test, reaching 75%. Student engagement also improved positively, reflecting the effectiveness of the learning model. The use of GeoGebra as a tool for visualization and exploration had a positive impact on deeper understanding. In conclusion, the Islamic context-based problem-solving learning model assisted by GeoGebra effectively enhances students' numeracy literacy. Keywords: problem-based learning, numeracy literacy, Islamic context, GeoGebra

Pengaruh Model Context-Based Learning Berbantuan Simulasi PhET terhadap Keterampilan Berpikir Kritis pada Materi Elastisitas dan Hukum Hooke

The main focus of this research is centered on the issue of low critical thinking skills among high school students, particularly in the study of elasticity and Hooke's law. The research aims to investigate how a context-based learning model with the assistance of PhET simulations influences students' critical thinking skills in the context of elasticity and Hooke's law. The study was conducted at SMAS Cengkareng 1 Jakarta for five weeks in October 2023, involving a sample of 30 students. The sample selection utilized purposive sampling, comprising 30 students from class XI MIPA B (control group) and class XI MIPA A (experimental group). The applied design was quasi-experimental with a Nonequivalent Control Group Design. Data collection involved a critical thinking test instrument consisting of twelve essay-type questions (validated with a validity of 0.86 and reliability of 0.83). The results of the posttest hypothesis test using the t-test at a significance level of 0.05 showed a sig.(2-tailed) value of 0.000, with the conclusion H0 rejected (indicating the influence of the context-based learning model with PhET simulation assistance on students' critical thinking skills). The critical thinking of the experimental group showed a greater improvement (N-gain 0.459 in the moderate category) compared to the control group (N-gain 0.288 in the low category). The implications of this research encourage educators to implement learning models to enhance low critical thinking skills, allowing students to actively engage with complex challenges related to real-life contexts. The study underscores the need to improve critical thinking skills across various subjects.

Meningkatkan Keterampilan Berpikir Kreatif Melalui Model Pembelajaran Context-Based Learning (CBL) Pada Materi Dinamika Gerak

One of the troubles in Indonesian training is that the system of tutorial development remains not substantial and is abstract and even a way from actual existence, making it tough to train college students' creative wondering abilties. This study targets to enhance innovative questioning talents using the context-based learning (CBL) studying model on motion dynamics fabric. There are several articles associated with these studies for analysis. The sample for this research was 64 students taken from 2 classes, namely class XI K and XI L among 6 class XI at SMAN 3 Pandeglang. The methodology employed for data analysis to ascertain an increase in creative thinking skills in students is the N-gain test. Based on the N-gain analysis, the findings indicated an improvement in creative thinking skills using the context-based learning (CBL) learning model on Motion Dynamics material. This was obtained derived from the outcomes of statistical calculations which indicated the presence of increase in the value of N-gain in the class undergoing the experiment by 0.84 in the high category, while in the control class the N-gain value was only 0.39 falling within the moderate category. Therefore, learning using the context-based learning model is recommended to be employed to enhance the creative thinking abilities of students, especially in contextually related materials such as Motion Dynamics material.

Novel approach for the design of attack-aware context-based blockchain model

Novel approach for the design of attack-aware context-based blockchain model

Efficient centralized traffic grid signal control based on meta-reinforcement learning

Dear Editor, This letter presents a novel method to tackle the two challenges of the centralized traffic control based on reinforcement learning (RL): the curse of dimensionality as the scale of traffic grid increases and the data-inefficiency problem that requires large amounts of samples to learn. First, we use a sequence-to-sequence (seq2seq) model and the attention mechanism to decompose the state-action space into sub-spaces, thus dealing with the first challenge. Second, we propose a new context-based meta-RL model that disentangles task inference and control, which improves the meta-training efficiency and accelerates the learning process in the new environment. We evaluate our approach on real-world datasets and the results demonstrate that our approach outperforms the state-of-the-art deep reinforcement learning (DRL)-based methods and the traditional control methods.

The Application and Analysis of Context-Based Teaching Model in Chinese Reading Teaching of Junior Middle School—Taking <i>The Opera Performance in the Village </i>in the Textbook for Grade 8 as an Example

The Application and Analysis of Context-Based Teaching Model in Chinese Reading Teaching of Junior Middle School—Taking <i>The Opera Performance in the Village </i>in the Textbook for Grade 8 as an Example

ENHANCEMENT OF BRAND LOVE RESONANCE CAPABILITIES ON DESTINATION LOYALTY: S-DL PERSPECTIVE

The emergence of inconsistencies in study findings regarding destination brands does not contribute to an increase in customer loyalty toward tourist destinations. Furthermore, exploring the capacity to expand affection for the brand is a meaningful topic within the tourism business phenomenon. Therefore, it is crucial to examine how well the tourism business can reflect the affection for the destination brand. This study aimed to explore and develop the conceptual and empirical relationship between brand authenticity and destination loyalty. To conduct the analysis, a quantitative method was selected, using a sample of 297 respondents in Central Java, Indonesia. The data collected from distributing questionnaires were then processed using SEM-PLS software. Furthermore, the study was grounded in the Service-Dominant Logic perspective and focused on operant resources with the potential to enhance the value of natural destinations by capturing the emotional resonance of the destination brand. The results showed that loyalty goals were empirically and practically increased by creating a context-based model and leveraging the capability of brand love resonance. The approach addressed the gaps in prior studies and provided tourism actors with the means to consider external characteristics.

An information fusion based approach to context-based fine-tuning of GPT models

In the new era of Artificial Intelligence (AI), Generative Pre-Trained Transformer (GPT) has emerged as a central technique for generating human-like texts. Over recent years, there has been a growing trend towards using GPT for building chatbot systems. However, pre-trained GPT models lack context awareness which can result in awkward dialogue in specific contexts. In this research, we propose a new information fusion based approach to fine tuning GPT models based on contextual data and two scenarios of evidence combination by means of Dempster–Shafer theory of evidence. To this end, we first design a Transformers-based dialog classification model to be trained with the contextual data, which is then used jointly with additional pre-trained models as sources of evidence for judging the output of a GPT model as a context-appropriate response. Two scenarios for modeling and combining evidence provided by the context-based dialog classification model and pre-trained models are also proposed. We conduct a set of experiments on several datasets associated with specific contexts to demonstrate the effectiveness of the proposed approach. The empirical results show that it can improve the contextuality of general GPT-2 and GPT-3.5 models in most cases of the testing datasets.

Context is not key: Detecting Alzheimer’s disease with both classical and transformer-based neural language models

Natural language processing (NLP) has exhibited potential in detecting Alzheimer’s disease (AD) and related dementias, particularly due to the impact of AD on spontaneous speech. Recent research has emphasized the significance of context-based models, such as Bidirectional Encoder Representations from Transformers (BERT). However, these models often come at the expense of increased complexity and computational requirements, which are not always accessible. In light of these considerations, we propose a straightforward and efficient word2vec-based model for AD detection, and evaluate it on the Alzheimer’s Dementia Recognition through Spontaneous Speech (ADReSS) challenge dataset. Additionally, we explore the efficacy of fusing our model with classic linguistic features and compare this to other contextual models by fine-tuning BERT-based and Generative Pre-training Transformer (GPT) sequence classification models. We find that simpler models achieve a remarkable accuracy of 92% in classifying AD cases, along with a root mean square error of 4.21 in estimating Mini-Mental Status Examination (MMSE) scores. Notably, our models outperform all state-of-the-art models in the literature for classifying AD cases and estimating MMSE scores, including contextual language models.

LQMPCS: Design of a Low-Complexity Q-Learning Model based on Proof-of-Context Consensus for Scalable Side Chains

Single-chained blockchains are being rapidly replaced by sidechains (or sharded chains), due to their high QoS (Quality of Service), and low complexity characteristics. Existing sidechaining models use context-specific machine-learning optimization techniques, which limits their scalability when applied to real-time use cases. Moreover, these models are also highly complex and require constant reconfigurations when applied to dynamic deployment scenarios. To overcome these issues, this text proposes design of a novel low-complexity Q-Learning Model based on Proof-of-Context (PoC) consensus for scalable sidechains. The proposed model initially describes a Q-Learning method for sidechain formation, which assists in maintaining high scalability even under large-scale traffic scenarios. This model is cascaded with a novel Proof-of-Context based consensus that is capable of representing input data into context-independent formats. These formats assist in providing high-speed consensus, which is uses intent of data, instead of the data samples. To estimate this intent, a set of context-based classification models are used, which assist in representing input data samples into distinctive categories. These models include feature representation via Long-Short-Term-Memory (LSTM), and classification via 1D Convolutional Neural Networks (CNNs), that can be used for heterogeneous application scenarios. Due to representation of input data samples into context-based categories, the proposed model is able to reduce mining delay by 8.3%, reduce energy needed for mining by 2.9%, while maintaining higher throughput, and lower mining jitters when compared with standard sidechaining techniques under similar use cases.

A Context-based Question Selection Model to Support the Adaptive Assessment of Learning: A study of online learning assessment in elementary schools in Indonesia

A Context-based Question Selection Model to Support the Adaptive Assessment of Learning: A study of online learning assessment in elementary schools in Indonesia

A structured approach for resilience-oriented human performance assessment and prediction in offshore safety-critical operations

Resilience engineering is in the ascendant across diverse disciplines and research fields. Researchers and engineers work to create resilient systems and emphasize human elements for providing resilience. It prompts a new paradigm for viewing human performance in socio-technical systems, where variability is regarded as a measure of the potential for resilient human performance. This paper proposes a structured approach for resilience-oriented human performance assessment and prediction in offshore safety-critical operations. In the structured framework, a context-based Bayesian network model is developed to evaluate the internal and external variability of operator functions. For this purpose, a taxonomy of variability shaping factors is developed for operations at sea involving human-automation collaboration. Further , this study proposes integrating the functional resonance analysis method (FRAM) and fuzzy cognitive maps (FCM) to model the dynamic interaction of operator functions and simulate the coupling of operator variability in performing safety-critical operations. The integration augments both methods to tackle human performance modeling from both qualitative and quantitative aspects. A case study is presented in the context of offshore dynamic positioning operations to demonstrate the applicability of the approach. The results confirm that it provides a useful tool for managing operator variability and resilient performance to improve human-related safety.

Building a Resilient Event Sector in Times of Uncertainty

This article offers a novel context-based resilience model to identify and discuss the sources of the resilience of event ecosystems in times of the COVID-19 pandemic. In particular, the study employs a mixed-method (quantitative and qualitative) approach to study how event ecosystems have evolved due to the COVID-19 pandemic, while special attention is given to their adaptability and transformability capacities. A better understanding of how event ecosystems change under unexpected circumstances will promote solutions that strengthen the sector's resilience in a global pandemic and the postpandemic reality. Therefore, the research contributes twofold to the existing body of literature on event crisis management. First, it proposes a context-based event ecosystem resilience framework (CEER) to enable a more comprehensive understanding of what makes event ecosystems resilient and responsive to the changing environment in which they operate. Second, it takes on a challenge to conduct an empirical investigation of various aspects of the CEER model in the context of the COVID-19 pandemic.

Semantic Clone Detection Based on Code Feature Fusion Learning

Code clones are duplicated code snippets that significantly threaten software maintenance and the public corpora of code representation learning. Traditionally, code context and its structure information abstract syntax tree (AST), control flow graph (CFG) are typical representations of source code, and context-based models and structure-based models contributed significantly to the development of code clone detection. In this paper, we present a hybrid embedding model for code clone detection (HEM-CCD), a fusion method of token sequential information and graph-based structure information. We insert tokens’ global context information encoded by a bi-directional recurrent neural network into the AST-based graph for comprehensive code semantic representation. Then, feeding the graph into a gated graph neural network we generate code semantic vectors for similarity evaluation. We have implemented our model on two public clone datasets (BigCloneBench and GoogleCodeJam), and the results indicate that HEM-CCD outperforms several state-of-the-art approaches.

Abstract 5360: Improved identification of CHIP mutations from cell-free DNA without matched normal samples using machine learning

Abstract Blood-based liquid biopsies are becoming the standard for noninvasive profiling of tumors from circulating tumor DNA (ctDNA) to facilitate treatment decisions. However, it is known that non-tumor-derived mutations, such as those from clonal hematopoietic (CH) lineage, are also present in cell-free DNA (cfDNA) and can complicate tumor mutation detection and interpretation. Filtering alterations associated with CH can reduce the risk of false positives in ctDNA analysis. Current strategies for filtering CH mutations are based on prior knowledge of those in canonical CH genes, sequencing a matched peripheral blood mononuclear cell sample, or using multiple time points to evaluate changes in allele frequency (AF), with the assumption that AF of CH of indeterminate potential (CHIPs) are unlikely to be impacted by treatments; however, these approaches are either insufficient or cost prohibited. We present a machine learning framework that incorporates prior knowledge from tumor and blood-derived mutations to improve the classification of CH and tumor variants from plasma-only cfDNA samples and that greatly outperforms a simple rule-based approach. We developed a classification framework composed of three main modules to maximize the information gain from ctDNA as non-ctDNA samples: (1) a sequence context-based model where each variant is represented by 128 mutational signatures extracted using a bag-of-words-like modeling (using normal and tumor samples) along with mutational function effect scores from SnpSift and cancer type origin. In total 6,745 CH and 57,210 somatic tumor-derived mutations are used to train a model to distinguish CH, CH-putative cancer driver, and tumor mutations; (2) a ctDNA-context model that includes patient-level information (age, mutational signatures, and variant allele frequencies) from 392 tumor and 481 CH blood-derived mutations extracted exclusively from ctDNA samples to predict blood or tumor origin; and (3) a meta-classifier where models 1 and 2 are aggregated into a single score for predicting the origin of a given mutation. We compared our models against a baseline strategy where mutations were predicted as CH if they fell into a list of 27 canonical CH-related genes. Our proposed metaclassifier that integrates sequence- and ctDNA-based features improved the overall performance of CH detection (AUC = 0.83, compared with the baseline model, AUC = 0.72), highlighting the value of adding ctDNA-derived and mutation-context features to profile somatic mutations. As ctDNA assays and technologies rapidly progress and gain adoption for precision oncology, we anticipate that our work, combined with existing benchmarking efforts, will enable more robust analyses on critical applications such as early disease detection, minimal residual disease, and mutational profiling to ultimately best inform clinical decision-making. Citation Format: Gustavo Arango-Argoty, Gerald Sun, Aleksandra Markovets, Carl Barrett, Zhongwu Lai, Etai Jacob. Improved identification of CHIP mutations from cell-free DNA without matched normal samples using machine learning. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5360.

Countering Violent Extremism and Combating Models in Pakistan

Countering Violent Extremism (CVE) has become redoubtable task to combat the amorphous nature of conflicts around the globe. Extremism is a daunting phenomenon which requires persistent efforts at societal, political and socio-economic level to counter the recurrence, resurgence and involvement of hostile extraneous agencies to disrupt the peace in Pakistan. The study familiarizes the reader with the application of non-kinetic measures to counter violent extremism through deradicalization and undercutting the ideological and violent discourse narratives. This study is an endeavour to construct a context-based and multi pronged model of countering violent extremism in Pakistan. Counter violent extremism (CVE) helps in combating the scourge of terrorism through countering the extremist narrative, prevention, rehabilitation and after care programmes of terrorists. This research helps in devising the CVE models to deal extremism on Macro, Meso and Micro level. The research methodology is qualitative, based on the study of research journals, article, books, newspapers, non-structured interviews and personal reflections.

Hyperparameter optimization through context-based meta-reinforcement learning with task-aware representation

In this paper, we combine context-based Meta-Reinforcement Learning with task-aware representation to efficiently overcome data-inefficiency and limited generalization in the hyperparameter optimization problem. First, we propose a new context-based meta-RL model that disentangles task inference and control, which improves the meta-training efficiency and accelerates the learning process for unseen tasks. Second, the task properties are inferred on-line, which includes not only the dataset representation but also the task-solving experience, thus encouraging the agent to explore in a much smarter fashion. Third, we employ amortized meta-learning to meta-train the agent, which is simple and runs faster than the gradient-based meta-training method. Experimental results suggest that our method can search for the optimal hyperparameter configuration with limited computational cost in a reasonable time.

Adult age differences in subjective context retrieval in dual-list free recall

ABSTRACT Age-related episodic memory deficits imply that older and younger adults differentially retrieve and monitor contextual features that indicate the source of studied information. Such differences have been shown in subjective reports during recognition and cued recall as well as process estimates derived from computational models of free recall organisation. The present study extends the subject report method to free recall to characterise age differences in context retrieval and monitoring, and to test assumptions from a context-based computational model. Older and younger adults studied two lists of semantically related words and then recalled from only the first or second list. After each recall, participants indicated their subjective context retrieval using remember/know judgments. Compared to younger adults, older adults showed lower recall accuracy and subjective reports of context retrieval (i.e., remember judgments) that were less specific to correct recalls. These differences appeared after first-recall attempts. Recall functions conditioned on serial positions were more continual across correct recalls from target lists and intrusions from non-target lists for older than younger adults. Together with other analyses of context retrieval and monitoring reported here, these findings suggest that older adults retrieved context less distinctively across the recall period, leading to greater perceived similarity for temporally contiguous lists.