Contextual Clues Research Articles

Comparing Implicit and Explicit Morphological Analysis Instruction for Upper Elementary Readers

ABSTRACT Purpose We compared the effects of two types of instruction on novel suffix learning. Instruction differed in the explicitness of the attention given to the morphological structure of the targets. Learning in form and meaning was measured in trained and transfer words. Method Over three days Grade 3 (N = 83, 45.8% females, M age = 8.4) and Grade 5 (N = 86, 47.7% females, M age = 10.4) students with English as their first language (93% caucasian, 4% East Asian, 2% Latino, 1% First Nations, Metis and Inuit) received training on the definitions of pseudowords with a salient morphological structure (e.g. nim meaning small in “hillnim”). Training activities explicitly taught the morphological structure of the words, or exposed participants to this structure implicitly while teaching the use of general context clues. Participants’ learning was assessed immediately (one day) after training, and at follow-up one week later, using a suffix identification task, a word definition, and a multiple-choice task. Results Participants at both grade levels scored similarly on the form task across conditions, but in terms of meaning, explicit training on morphological structure yielded better results for both grade levels and word types. However, for Grade 5 the differences across training conditions were only significant in the word definition task. Conclusion Our results support explicitly teaching the structure of morphologically complex words. Explicit instruction was effective even for older students with more reading experience.

Comparative Study of GPT-4Vision and Convolutional Neural Networks in Histopathological Image Analysis

Abstract Introduction/Objective Recent advancements in artificial intelligence (AI), particularly with Large Language Models like ChatGPT-4Vision (GPT-4V), have expanded the potential for automated interpretation of medical images. This study evaluates the diagnostic accuracy of GPT-4V in histopathological analysis of neurodegenerative diseases and compares its performance with traditional Convolutional Neural Networks (CNNs). Methods/Case Report We utilized 1515 histopathological images, including hematoxylin and eosin (H&E) staining and tau immunohistochemistry, from patients with various neurodegenerative diseases, such as Alzheimer’s disease, progressive supranuclear palsy, and corticobasal degeneration. GPT-4V’s performance was assessed through multi- step prompts to assess how textual context influences the image interpretation. In the quantitative analysis, the diagnostic accuracy of GPT-4V and the CNN-based model YOLOv8 was assessed for classifying three tau lesions—astrocytic plaques, neuritic plaques, and tufted astrocytes. The analysis used both zero-shot and few-shot learning methods, where GPT-4V and YOLOv8 were evaluated on their ability to distinguish these lesions using ten hold-out test images and 490 training images per lesion. Results (if a Case Study enter NA) GPT-4V accurately recognized Lewy bodies in all cases, but it showed limited accuracy in identifying neurofibrillary tangles (20%) and Pick bodies (0%) on H&E slides. Notably, GPT-4V often suggested Alzheimer’s disease as a potential diagnosis, relying more on contextual clues, such as the prevalence of certain diseases, rather than direct image analysis. In the tau immunohistochemical analysis, GPT-4V’s diagnoses were strongly influenced by additional information about staining and brain region. However, few-shot learning markedly improved GPT-4V’s diagnostic capabilities from 40% accuracy with zero-shot learning to 90% with few-shot learning, reaching best accuracy with 20-shot learning. This performance compared favorably against YOLOv8, which also achieved 90% accuracy but required 100-shot learning. Conclusion While GPT-4V faces challenges in independently interpreting histopathological images, few-shot learning significantly improves its accuracy. This approach is particularly promising for neuropathology, where acquiring extensive labeled datasets is challenging. The findings highlight the need for ongoing refinement of AI applications in pathology, emphasizing balanced integration of textual and visual data to optimize diagnostic efficacy and reliability.

Predicting Latin stories: an investigation of contextual clues

Predicting Latin stories: an investigation of contextual clues

MAKNA PENAMAAN SAJIAN RITUAL KABASA DARI PERSPEKTIF GASTROLINGUISTIK

This article is about Munanese (an ethnic group in Southeast Sulawesi) traditional dish kabasa as a form of referential support to the gastrodiplomacy trend. Kabasa is a part of the haroa ritual that features a range of dishes with various ingredients, which is strongly connected to various aspects of the community such as social position, psychology, and environment. In this study, a combination between linguistics as language science and gastronomy which examines food within its culture is considered effective for investigating the semantics of the naming pattern of kabasa dishes in order to document this unique culinary tradition and introduce new concepts in Indonesian gastronomy, particularly that of Southeast Sulawesi. Using the qualitative descriptive method and macrolinguistic approach, this study shows that kabasa naming pattern consists of three semantic levels. Meanwhile, more comprehensive gastrolinguistic analysis on the data suggests that the meanings of the names can be found based on (1) the food ingredients, which are tied to the region’s geographical features and (2) the semantic convention of kabasa itself, which shows an associative relationship between an object, its name, and environmental aspects as contextual clues.

ICA-CRMAS: Intelligent Context-Awareness Approach for Citation Recommendation based on Multi-Agent System

Navigating the ever-expanding sea of scientific literature presents a daunting challenge for researchers seeking relevant and up-to-date information. Traditional citation recommendation systems, while well-intentioned, often fall short due to their limited focus on text-based features and lack of contextual awareness. In this paper we introduce the ICA-CRMAS (Intelligent Context-Aware Approach for Citation Recommendation based on Multi-Agent System), an intelligent system that leverages the power of deep learning, semantic analysis, and multimodal learning to overcome these limitations. ICA-CRMAS goes beyond the surface, delving into the rich tapestry of information within academic papers, including figures, which often hold vital contextual clues. By weaving this contextual data directly into its recommendation models, ICA-CRMAS generates highly personalized and relevant suggestions. This comprehensive approach unlocks enhanced accuracy, diversity, and serendipity, enabling researchers to effectively discover papers aligning with their interests and research objectives. ICA-CRMAS illuminates its reasoning. Instead of opaque suggestions, the system provides clear explanations that justify and illustrate recommended citations. This transparency builds user confidence, allowing researchers to critically engage with and trust the system’s recommendations. Evaluation experiments conducted on real-world academic datasets demonstrate that ICA-CRMAS outperforms existing approaches across various metrics. it surpassing its closest competitor by a margin of 7.53 on accuracy, 6.07% on MRR and by 5.87 on Recall. User feedback further reinforces its effectiveness, with an Overall System Usability Scale (SUS) score of 76.73, exceeding benchmark scores for comparable systems.

The impact of subtle contextual information on remembering to complete academic goals.

Remembering to complete goals-termed prospective memory (PM)-is critical for success in everyday life, yet minimal empirical work has been dedicated to examining PM within an educational setting. The main goal of this study was to investigate students' ability to complete numerous future-oriented academic intentions (PM tasks) while simultaneously paying attention to a lecture and to see if working memory (WM) capacity and adding subtle contextual information would support the students' likelihood of completing their PM tasks. Participants took part in a 2-hr session of college course-like activities. Throughout the session, there was occasionally the opportunity to complete one of several naturalistic PM tasks. The following findings are based on the results of our Bayesian models. Providing subtle contextual clues about when PM tasks could be completed was found to likely increase performance. The number of PM intentions to be remembered (i.e., load) produced no discernable effect on ongoing task performance or PM performance. Furthermore, individual differences in WM capacity were likely to be predictive of a near-zero change in PM performance. The current findings hold meaningful implications for educators, wherein providing context, even at a subtle level, can enhance students' ability to remember to complete tasks, without altering their ability to focus on the tasks at hand. Moreover, it appears that asking students to remember to complete multiple, prospective in-class tasks is not likely to hinder task completion or their ability to focus on other ongoing tasks.

The enemy within: The new war in medical education

IntroductionWar and military metaphors have long been used in clinical medicine to describe medicine's collective fight against disease. However, recently resistor trainees have used similar language to describe their acts of professional resistance against social harm and injustice. To understand the contours of this war, this study analyzes the metaphoric language these trainees use to describe their acts of resistance. MethodsWe recruited 18 resisting trainees using our personal and professional networks and snowball sampling. Participants were interviewed from July 2022–February 2023. Using methodological bricolage, we analyzed the data using Wickens' analytical approach, which draws on constant comparative analysis and discursive textual analysis. Data were analyzed in three phases that included a consult with a military historian, isolation of metaphoric language, and a textual analysis using context clues from participants’ descriptions of their acts of professional resistance. ResultsResisting trainees used metaphorical language to signal an insurgency to topple power. These trainees referenced two conflicts: the mistreatment of patients and the mistreatment of trainees. Enemies were conceptualized as anyone who actively protects institutions and the traditions of medicine, such as leaders of medical schools and hospitals, and physicians trained in a more traditional system. Trainees conceptualized the primary battlefield as medicine's process of socialization that integrates trainees into a profession, and accepts mistreatment as the norm. Weapons included LCME site visits and sympathetic faculty members. ConclusionWhereas metaphorical language around war and the military was previously the purview of physicians, resistor trainees have adopted war metaphors for their own purposes. They do not use these metaphors accidently; they are meant to signal their intentions to restructure medical education. Leaders must begin working with trainees in sincere partnership to create widespread change.

Academic Vocabulary Learning in EMI Classrooms: Challenges and Strategies

This study investigates English as a Second Language (ESL) learners’ challenges in understanding academic science vocabulary in Malaysian secondary schools’ English-medium instruction (EMI) classrooms. Based on cognitive load theory, the research explores ESL learners’ strategies to acquire scientific terminology and manage the associated cognitive difficulties. Previous research on EMI across different subjects has often overlooked the specific hurdles presented by technical terms in the scientific vocabulary, creating a knowledge gap that impedes the development of effective teaching methods for science educators in EMI settings. In response, the study adopts a two-stage qualitative design, commencing with an online survey involving 103 participants, followed by group interviews with individuals from diverse linguistic backgrounds. The findings indicate that ESL learners in EMI science classrooms encounter significant obstacles due to unfamiliar scientific terminology, abstract concepts, and limited prior exposure to the subject matter. This results in a high cognitive load that inhibits comprehension. Nevertheless, the research identifies various vocabulary learning strategies students employ, including contextual clues and active engagement with scientific vocabulary within relevant contexts. These findings underscore the need for more inclusive learning environments supporting vocabulary development for ESL learners in EMI settings. Recommendations encompass creating instructional materials featuring clear and concise language and integrating scaffolding strategies to simplify complex scientific terms. This comprehensive approach has the potential to facilitate successful vocabulary development and enhance the overall learning experience for ESL learners in EMI science classrooms.

Reading Comprehension Levels and Skills of Filipino Grade 8 Students: A Basis for Catch-up Friday Activities

Despite the global emphasis on literacy, the Philippines continues to lag in reading proficiency, as evidenced by international assessments. A new program, "Catch-Up Fridays," has been introduced by the Department of Education, providing dedicated time for students to focus on improving their reading skills and addressing gaps in comprehension. This research aims to identify specific areas needing intervention to improve students' reading skills and to enhance their critical 21st-century skills such as critical thinking, problem-solving, and effective communication. Specifically, this study investigated the reading comprehension levels and skills of Grade 8 students at Baguio City National Science High School, focusing on the impacts of gender and the use of English as a primary language at home. Employing a quantitative descriptive research design, the study assessed 150 students using a questionnaire adapted from the Philippine Informal Reading Inventory (Phil-IRI). Results indicate that most of the students are at the instructional (48.67%) and independent (48.00%) reading levels, with a mean comprehension score of 79%. Gender analysis reveals males generally outperform females, while language analysis shows students using English at home have higher comprehension levels than their peers. The least mastered skill is using context clues, with inferential skills such as predicting outcomes and making inferences also needing improvement. The findings emphasize the need for targeted interventions to address least mastered skills, particularly for students at the frustration level, and highlight the importance of addressing gender disparities and supporting non-English-speaking students.

‘A better sort of reader’: Wittgenstein on literary reading

AbstractF. R. Leavis, the leading literary critic at Cambridge from 1930 to 1960, recounts the time when his friend Ludwig Wittgenstein told him to ‘give up literary criticism!’ The remark came as a surprise to Leavis, and it remains somewhat puzzling to anyone who reads of the encounter, for there are few contextual clues as to why Wittgenstein would say such a thing. But there are clues in Wittgenstein's many remarks on literature, music and other art forms scattered throughout his writings. In this paper, I will present what I think Wittgenstein's concern was with the sort of literary criticism Leavis practised, contrasting it with the sort of reading, and the sort of reader, that Wittgenstein (and others) would consider a literary way of reading; the sort of reader he thought was ‘a better sort of reader’.

Masked Image Modeling Auxiliary Pseudo-Label Propagation with a Clustering Central Rectification Strategy for Cross-Scene Classification

Cross-scene classification focuses on setting up an effective domain adaptation (DA) way to transfer the learnable knowledge from source to target domain, which can be reasonably achieved through the pseudo-label propagation procedure. However, it is hard to bridge the objective existing severe domain discrepancy between source and target domains, and thus, there are several unreliable pseudo-labels generated in target domain and involved into pseudo-label propagation procedure, which would lead to unreliable error accumulation to deteriorate the performance of cross-scene classification. Therefore, in this paper, a novel Masked Image Modeling Auxiliary Pseudo-Label Propagation called MIM-AP2 with clustering central rectification strategy is proposed to improve the quality of pseudo-label propagation for cross-scene classification. First, in order to gracefully bridge the domain discrepancy and improve DA representation ability in-domain, a supervised class-token contrastive learning is designed to find the more consistent contextual clues to achieve knowledge transfer learning from source to target domain. At the same time, it is also incorporated with a self-supervised MIM mechanism according to a low random masking ratio to capture domain-specific information for improving the discriminability in-domain, which can lay a solid foundation for high-quality pseudo-label generation. Second, aiming to alleviate the impact of unreliable error accumulation, a clustering central rectification strategy is designed to adaptively update robustness clustering central representations to assist in rectifying unreliable pseudo-labels and learning a superior target domain specific classifier for cross-scene classification. Finally, extensive experiments are conducted on six cross-scene classification benchmarks, and the results are superior to other DA methods. The average accuracy reached 95.79%, which represents a 21.87% improvement over the baseline. This demonstrates that the proposed MIM-AP2 can provide significantly improved performance.

Utilizing Machine Learning for Context-Aware Digital Biomarker of Stress in Older Adults

Identifying stress in older adults is a crucial field of research in health and well-being. This allows us to take timely preventive measures that can help save lives. That is why a nonobtrusive way of accurate and precise stress detection is necessary. Researchers have proposed many statistical measurements to associate stress with sensor readings from digital biomarkers. With the recent progress of Artificial Intelligence in the healthcare domain, the application of machine learning is showing promising results in stress detection. Still, the viability of machine learning for digital biomarkers of stress is under-explored. In this work, we first investigate the performance of a supervised machine learning algorithm (Random Forest) with manual feature engineering for stress detection with contextual information. The concentration of salivary cortisol was used as the golden standard here. Our framework categorizes stress into No Stress, Low Stress, and High Stress by analyzing digital biomarkers gathered from wearable sensors. We also provide a thorough knowledge of stress in older adults by combining physiological data obtained from wearable sensors with contextual clues from a stress protocol. Our context-aware machine learning model, using sensor fusion, achieved a macroaverage F-1 score of 0.937 and an accuracy of 92.48% in identifying three stress levels. We further extend our work to get rid of the burden of manual feature engineering. We explore Convolutional Neural Network (CNN)-based feature encoder and cortisol biomarkers to detect stress using contextual information. We provide an in-depth look at the CNN-based feature encoder, which effectively separates useful features from physiological inputs. Both of our proposed frameworks, i.e., Random Forest with engineered features and a Fully Connected Network with CNN-based features validate that the integration of digital biomarkers of stress can provide more insight into the stress response even without any self-reporting or caregiver labels. Our method with sensor fusion shows an accuracy and F-1 score of 83.7797% and 0.7552, respectively, without context and 96.7525% accuracy and 0.9745 F-1 score with context, which also constitutes a 4% increase in accuracy and a 0.04 increase in F-1 score from RF.

How readers attentive and inattentive to task-related information scan: An eye-tracking study

PurposeAttentive to task-related information should be the highest priority for all readers engaged in task-reading. Investigating the scanning behaviors of attentive versus inattentive readers shed new insights into the sequential cognitive processes, but it has seldom been studied. This study investigates their global patterns on a global scale, pertaining to the whole length of scanpaths, and further compares local tactics, local strategies, and local strategy transitions on a local scale, related to the isolated regions of scanpath. Design/methodology/approachA regular style reading system with the question, navigating, and text areas on its interface, and two types of task, namely fact-finding (FF) and content understanding (CU), were designed in an eye-tracking experiment. 24 participants were placed into attentive (AR) or inattentive (IAR) readers groups according to their fixation duration on task-related paragraphs. A global sequence analysis algorithm, Needleman-Wunsch, was applied to uncover global patterns across the whole length of scanpaths (whole-scanpaths). A local sequence analysis method related to frequent sub-scanpaths was adopted to extract local tactics specific to the reader and task. Coding was performed to identify local strategies by classifying local tactics. A local strategy transition was further identified as a sequence of frequent local strategies at the beginning, middle, and ending phases. FindingsWhole-scanpaths of AR significantly differed from those of IAR, despite the absence of global patterns for each group. Five types of local strategy were identified, namely locating information(LI), evaluating and verifying text relevance (EVR), navigation heuristics (NH), synthesizing information(SI), and contextual clues (CC). AR applied all types in both tasks, whereas IAR applied only two types and stuck with EVR. Furthermore, two types of local strategy transition were identified: comprehensive exploration and iterative content evaluation. AR employed the former with the linear feature in FF and the spiral feature in CU, while IAR employed the latter in both tasks. OriginalityThis study advances the knowledge of dynamic cognitive processing from an attentive and inattentive to task-related information perspective. An objective analysis perspective for obtaining global patterns, local tactics, local strategies, and local strategy transitions is provided, then it can provide new insights into automatically classifying readers. The results also generate detailed and valuable guidance for improving reading system design and training readers.

English Language Teaching Strategies in Developmentally Appropriate Practice

This research aimed to know what are English Language Teaching Strategies in Developmentally Appropriate Practice. developmentally appropriate practice was a learning activity that recognized that each student had different needs and abilities. In developmentally appropriate practice, students were provided with various choices in terms of learning materials, teaching methods, and assessment. The qualitative research was a research design for this research. The study was conducted in SMPN 1. The subject of the research was an English teacher of SMPN 1 Tanjungpinang. The researcher used a purposive sampling technique to select the subject. The researcher only chose an English teacher for the subject of the study because in the school, there was only one teacher implementing developmentally appropriate practice. The research used observation, interview, and documentation as instruments to collect data. English Language Teaching Strategies in Developmentally Appropriate Learning used by teachers were Video-assisted learning (VAL), Picture Stories, Context Clues, and Group-Work strategies.

Context clues: Citizens and their higher valuation of the contextualist journalist role

This study examines media trust and how members of the public perceive and value various journalistic roles using a nationally representative survey (n = 1,026) to redefine and confirm how the American public places significant value on the contextualist role of journalism. The individual journalistic functions that make up contextual reporting are also highly valued. Media trust is primarily predicted by overall journalistic role perception, even above ideological views, but not for the contextualist role.

Τhe Meaning of ἀλληγορέω in Galatians 4.24 Revisited

AbstractThe meaning of the verb ἀλληγορέω stands at the heart of the debate concerning Paul's hermeneutic in Galatians 4.21–31. If by using the term Paul means ‘I am interpreting these things allegorically’, then the question of Paul's interpretive procedure would be all but answered – he would likely be allegorising as the Greeks did before him and the early church fathers did after. However, if he does not mean this, then the question remains open. This article argues that the phrase ἅτινά ἐστιν ἀλληγορούμɛνα means ‘these things are symbolic’, which would indeed leave this question open. This rendering is best for two reasons: First, the majority of the uses of ἀλληγορέω available in the two hundred or so years surrounding the writing of Galatians mean ‘to speak symbolically’. Second, the contextual clues surrounding Paul's use of the term in Galatians itself, such as his call to hear the law in verse 21, strongly suggest such a reading. To prove this thesis, this article provides detailed exposition of the texts in which ἀλληγορέω occurred around the time Paul wrote Galatians before turning to Paul's own use of the term in Galatians 4.24.

Data-driven diagnostics of boiling heat transfer on flat heaters from non-intrusive visualization

Rankine-based power generation cycles require the use of boiling heat exchangers for liquid–vapor phase change. However, operating boilers at too high heat fluxes can lead to heater meltdown due to onset of the boiling crisis. Despite recent advances in increasing this critical heat flux, the lack of fast and accurate diagnostics hinders boiling heat transfer safety at high heat fluxes. Here we show that neural networks fed on direct observation of bubble dynamics can be used to measure heat flux during nucleate boiling. The boiling heat transfer data was obtained with a horizontally oriented, circular flat heater with saturated water as the heat transfer fluid. A deep convolutional neural network (CNN) was trained using frames from videos acquired from 15 different heat dissipation values on the boiling curve to learn how these images correlate to the underlying heat flux. The CNN predicted heat fluxes with a mean square error of 14.6 W/cm2. Furthermore, the CNN presented high sensitivity to pixel intensity in image regions relevant to boiling heat transfer physics (e.g. bubble boundaries, contextual clues about bubble image distortion). Understanding this approach may allow future studies in unstable and critical boiling regimes that require fast transient heat flux diagnostics and cannot rely on slower thermohydraulic measurements.

PROJECT SAVE: AS A STRATEGY IN IMPROVING LITERACY THROUGH VOCABULARY INTEGRATION AMONG GRADE 8 LEARNERS

This study aimed to investigate if the vocabulary integration before discussion will be effective in improving literacy level of Grade 8 learners. Specifically, it answered the following research questions: (1) What is the level of the participants performance in the pre-test? (2) What is the level of the participants performance in the post test? (3) Is there a significant difference between the pre-test and post-test of the participants before and after the implementation of the vocabulary integration? (4) Based on the findings, what actions can be planned and implemented to improve the participants literacy level. The participants were 2 groups of Grade 8 learners. They were selected through purposive sampling which entails that the following criteria were met: (1) Grade 8 group of students with the least average in MPS in MELC number one which is Determine the meaning of words and expressions that reflect the local culture by noting context clues. (2) Most members of the class are struggling reader based on Phil-Iri result S.Y 2022-2023. A teacher-made pre-test was administered before presenting this proposed strategy, followed by a posttest after its utilization. Mean (M) and Standard Deviation (SD) were utilized to determine the level of students performance in scaling before and after applying Project SAVE. To determine the significant difference of students literacy level before and after applying Project SAVE, T- test for two dependent means (paired t-test) was used. The results showed that out of 83 students no one got excellent score, 14 very satisfactory and 34 satisfactory score on pretest as compared to posttest, 34 learners achieved excellent score, 35 very satisfactory and 13 satisfactory. The mean score increased significantly from satisfactory to very satisfactory, as well as the standard deviation. Therefore, it can be concluded that the implementation of Project SAVE using SIM has a positive impact on the learners literacy level. Thus, it is recommended for other Grade 8 learners and or other Grade levels. KEYWORDS: Project SAVE; Strategy for Learners Literacy Improvement.

Morphological assessment features and their relations to reading: A meta-analytic structural equation modeling study

Morphemes are the smallest meaningful units of language (e.g., affixes, base words) that express grammatical and semantic information. Additionally, morphological knowledge is significantly related to children's word reading and reading comprehension skills. Researchers have broadly assessed morphological knowledge by using a wide range of tasks and stimuli, which has influenced the interpretation of the relations between morphological knowledge and reading outcomes. This review of 103 studies used meta-analytic structural equation modeling (MASEM) to investigate the relations between commonly occurring morphological knowledge assessment features (e.g., written versus oral, spelling versus no spelling) in the literature to reading outcomes, including word reading and reading comprehension. Meta-regression techniques were used to examine moderators of age and reading ability. Morphological assessments that used a written modality (e.g., reading, writing) were more predictive of word reading outcomes than those administered orally. Assessments of morphological spelling were more predictive of both word reading and reading comprehension outcomes than those that did not examine spelling accuracy. Age was a significant moderator of the relation between morphology and word reading, such that the relation was stronger for the younger than the older children. Younger children also demonstrated higher relations between multiple task dimensions and reading comprehension, including oral tasks, tasks without decoding, and tasks that provided context clues. These findings have important implications for future morphological intervention studies aimed to improve children's reading outcomes, in particular the use of orthography and spelling within the context of teaching morphology.

The impact of internal-generated contextual clues on EFL vocabulary learning: insights from EEG.

With the popularity of learning vocabulary online among English as a Foreign Language (EFL) learners today, educators and researchers have been considering ways to enhance the effectiveness of this approach. Prior research has underscored the significance of contextual clues in vocabulary acquisition. However, few studies have compared the context provided by instructional materials and that generated by learners themselves. Hence, this present study sought to explore the impact of internal-generated contextual clues in comparison to those provided by instructional materials on EFL learners' online vocabulary acquisition. A total of 26 university students were enrolled and underwent electroencephalography (EEG). Based on a within-subjects design, all participants learned two groups of vocabulary words through a series of video clips under two conditions: one where the contexts were externally provided and the other where participants themselves generated the contexts. In this regard, participants were tasked with either viewing contextual clues presented on the screen or creating their own contextual clues for word comprehension. EEG signals were recorded during the learning process to explore neural activities, and post-tests were conducted to assess learning performance after each vocabulary learning session. Our behavioral results indicated that comprehending words with internal-generated contextual clues resulted in superior learning performance compared to using context provided by instructional materials. Furthermore, EEG data revealed that learners expended greater cognitive resources and mental effort in semantically integrating the meaning of words when they self-created contextual clues, as evidenced by stronger alpha and beta-band oscillations. Moreover, the stronger alpha-band oscillations and lower inter-subject correlation (ISC) among learners suggested that the generative task of creating context enhanced their top-down attentional control mechanisms and selective visual processing when learning vocabulary from videos. These findings underscored the positive effects of internal-generated contextual clues, indicating that instructors should encourage learners to construct their own contexts in online EFL vocabulary instruction rather than providing pre-defined contexts. Future research should aim to explore the limits and conditions of employing these two types of contextual clues in online EFL vocabulary learning. This could be achieved by manipulating the quality and understandability of contexts and considering learners' language proficiency levels.