Different Levels Of Representation Research Articles

EFFECT THE PROBLEM-BASED LEARNING MODEL ON ABILITY MULTIPLE REPRESENTATIONS OF XI-GRADE STUDENTS ON ACID-BASED TOPIC

It takes the ability to conceive at three different levels of representation, macroscopic, sub microscopic, and symbolic to comprehend acid-base material. Still, most students are unable to make the connection between the three levels of representation. As a result, instruction that helps students connect various representations in acid-base content is required. The PBL model is one appropriate learning paradigm that can be used. The purpose of this study is to determine whether PBL models and conventional models differ in their capacity to support numerous representations. A quantitative experiment using a quasi-experimental study design is the research methodology employed. A post-test-only control group design is used in this study. The samples of this study were XI MIPA 3 as a control group of 34 students and XI MIPA 4 as an experimental group of 33 students. Simple random sampling was used to get the research samples. A post-test consisting of multiple-choice questions was the instrument utilized. With a reliability score of 0,802 across 20 items, the empirical validation results indicate that all instruments are appropriate for use as research measures. methods for gathering data through tests. Data collection techniques in the form of tests. The data analysis technique in this study is descriptive and inferential statistics with hypothesis testing in the form of the Independent Sample t-Test. With a sig value of 0,000 < 0,05, the results demonstrated that there is a difference in the ability of multiple representations between those taught using the PBL model and those taught using the conventional model.

The impact of multiple-behavior HIV interventions as a function of regional disadvantages: An analysis of syndemics.

Disadvantaged populations, including inhabitants of developing countries as well as racial/ethnic and sexual minorities in the United States, are disproportionally burdened by human immunodeficiency virus (HIV) infection, delayed HIV diagnosis, and unfavorable HIV-treatment outcomes. HIV interventions targeting single behaviors (e.g., testing) in these populations have shown to be efficacious at producing behavioral and clinical change but have been unable to eliminate the social health disparities associated with syndemics (i.e., a set of connected risks, interacting synergistically, and contributing to excess burden of disease in a population). This meta-analysis of 331 reports (clusters; number of effect sizes [k] = 1,364) assessed whether multiple-behavior interventions that target clusters of syndemic risks are more efficacious for those in disadvantaged regions and social groups. Across the board, multiple-behavior interventions were more efficacious than single-behavior ones as well as passive control groups among samples from countries with lower log gross domestic product (GDP), lower Human Development Index (HDI), and lower Healthcare Access and Quality (HAQ) Index. Within the United States, the efficacy of multiple-behavior interventions was similar across different levels of representation of racial/ethnic and sexual minorities. The analyses used robust variance estimation with small-sample corrections to assess the differential effects of multiple-behavior interventions and Egger Sandwich test with the multilevel meta-analysis approach to detect selection biases. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Verbal working memory capacity modulates semantic and phonological prediction in spoken comprehension.

Mounting evidence suggests that people may use multiple cues to predict different levels of representation (e.g., semantic, syntactic, and phonological) during language comprehension. One question that has been less investigated is the relationship between general cognitive processing and the efficiency of prediction at various linguistic levels, such as semantic and phonological levels. To address this research gap, the present study investigated how working memory capacity (WMC) modulates different kinds of prediction behavior (i.e., semantic prediction and phonological prediction) in the visual world. Chinese speakers listened to the highly predictable sentences that contained a highly predictable target word, and viewed a visual display of objects. The visual display of objects contained a target object corresponding to the predictable word, a semantic or a phonological competitor that was semantically or phonologically related to the predictable word, and an unrelated object. We conducted a Chinese version of the reading span task to measure verbal WMC and grouped participants into high- and low-span groups. Participants showed semantic and phonological prediction with comparable size in both groups during language comprehension, with earlier semantic prediction in the high-span group, and a similar time course of phonological prediction in both groups. These results suggest that verbal working memory modulates predictive processing in language comprehension.

For the Most Part Serious?

Abstract Scholarship has drawn a contrast between modern uses of metalepsis (illogical transgressions of narrative levels), which are frequently assigned comic effects, and their ancient counterparts, which are deemed more serious. This article argues that in the case of authorial metalepsis, a way of expression presenting the narrator as bringing about the effects he describes, the comic potential was already discovered in antiquity. Case studies from Aristophanes, Plato, Horace, Juvenal, Ovid, and Lucian demonstrate how ancient authors use authorial metalepsis to evoke paradoxical scenes of interaction across different levels of representation that support a mocking presentation of other authors.

A Combination of Real-World Experiments and Augmented Reality When Learning about the States of Wax—An Eye-Tracking Study

Burning candles show the solid and liquid states of wax on a macroscopic level. With augmented reality, the submicroscopic and symbolic level of all three states of wax can be shown. The augmented reality environment developed in this study lets students test their knowledge about the position of the three states of wax. So far, how the design parameters of augmented reality learning environments influence users’ eye movement and learning performance has not been researched. Twenty-three German students between the ages of 9 and 15 form the randomized sample of this study with three different groups. AR learning scenarios were created, varying only in one design parameter: ‘congruence with reality’. Our analysis using audio, video, and eye-tracking data showed that all the participants learned mostly the same and that the participants who saw the real experiment on screen experienced the highest degree of immersion. This study indicates that the presented AR learning environment is an opportunity to learn about what exact part of a candle is burning with the submicroscopic level shown in comparison; before using the learning environment, the students were uncertain about what substance burns when a candle is lit and what function the wick has. This study suggests teachers should think about implementing learning environments such as this to help students connect different levels of representation.

Psycholinguistics: Cognitive Processes and Language Cross Interaction in Bilingualism in Educational Institutions

This article provides a brief review of psycholinguistic research that has been conducted to reveal the processes and representations of bilingualism that occur in a multicultural society, especially. The main conclusion from the description of the different levels of representation that has been presented both at the lexical level, semantic level, and syntactic level is that bilingual speakers always experience different languages as long as their use continues. Both bilingual speakers and speech partners benefit from the universal characteristics of the languages they master in an integrated manner. In line with this conclusion, recommendations that can be given regarding psycholinguistic research are on the study of language control and language transfer.

Scene Classification Based on Heterogeneous Features of Multi-Source Data

Remote sensing scene classification is quite important in earth observation and other fields. Previous research has found that most of the existing models are based on deep learning models. However, the classification accuracy of the deep learning model is difficult to break through due to the challenges of difficulty distinguishing the socio-economic attributes of scenes, high interclass similarity, and large intraclass differences. To tackle the challenges, we propose a novel scene classification model that integrates heterogeneous features of multi-source data. Firstly, a multi-granularity feature learning module is designed, which can conduct uniform grid sampling of images to learn multi-granularity features. In this module, in addition to the features of our previous research, we also supplemented the socio-economic semantic features of the scene, and attention-based pooling is introduced to achieve different levels of representation of images. Then, to reduce the dimension of the feature, we adopt the feature-level fusion method. Next, the maxout-based module is designed to fuse the features of different granularity and extract the most distinguishing second-order latent ontology essence features. The weighted adaptive fusion method is used to fuse all the features. Finally, the Lie Group Fisher algorithm is used for scene classification. Extensive experimentation and evaluations show that our proposed model can find better solutions to the above challenges.

An analysis from teaching interventions focused on levels of conceptualization

In this article, the didactic interventions of a future physics teacher are analyzed during the joint construction, with the group of students, of school scientific explanations of everyday phenomena in a secondary education classroom. The work aims to contribute to an under-researched territory, related to how teachers guide the construction of explanations in science classrooms. A case study focused on qualitative methodology was used, using thematic content analysis. Transcripts, class diaries and working sessions between residents and teachers were analyzed. An initial category system was built that was expanded inductively. A typology of discursive strategies used by the future teacher was developed, which includes strategies to promote conceptualization at different levels of representation of matter and meta-explanatory strategies to explicate aspects of the structure of explanations.

Are general circulation models obsolete?

Traditional general circulation models, or GCMs—that is, three-dimensional dynamical models with unresolved terms represented in equations with tunable parameters—have been a mainstay of climate research for several decades, and some of the pioneering studies have recently been recognized by a Nobel prize in Physics. Yet, there is considerable debate around their continuing role in the future. Frequently mentioned as limitations of GCMs are the structural error and uncertainty across models with different representations of unresolved scales and the fact that the models are tuned to reproduce certain aspects of the observed Earth. We consider these shortcomings in the context of a future generation of models that may address these issues through substantially higher resolution and detail, or through the use of machine learning techniques to match them better to observations, theory, and process models. It is our contention that calibration, far from being a weakness of models, is an essential element in the simulation of complex systems, and contributes to our understanding of their inner workings. Models can be calibrated to reveal both fine-scale detail and the global response to external perturbations. New methods enable us to articulate and improve the connections between the different levels of abstract representation of climate processes, and our understanding resides in an entire hierarchy of models where GCMs will continue to play a central role for the foreseeable future.

Developmental Network-2: The Autonomous Generation of Optimal Internal-Representation Hierarchy.

It is very challenging for machine learning methods to reach the goal of general-purpose learning since there are so many complicated situations in different tasks. The learning methods need to generate flexible internal representations for all scenarios met before. The hierarchical internal representation is considered as an efficient way to build such flexible representations. By hierarchy, we mean important local features in the input can be combined to form higher level features with more context. In this work, we analyze how our proposed general-purpose learning framework-the developmental network-2 (DN-2)-autonomously generates internal hierarchy with new mechanisms. Specifically, DN-2 incrementally allocates neuronal resources to different levels of representation during learning instead of handcrafting static boundaries among different levels of representation. We present the mathematical proof to demonstrate that optimal properties in terms of maximum likelihood (ML) are established under the conditions of limited learning experience and resources. The phoneme recognition and real-world visual navigation experiments that are of different modalities and include many different situations are designed to investigate general-purpose learning capability of DN-2. The experimental results show that DN-2 successfully learns different tasks. The formed internal hierarchical representations focus on important features, and the invariant abstract arise from optimal internal representations. We believe that DN-2 is in the right way toward fully autonomous learning.

Decoding syntactic class from EEG during spoken word recognition

A fundamental issue in spoken language comprehension involves understanding the interaction of linguistic representations across different levels of organization (e.g., phonological, lexical, syntactic, and semantic). In particular, there is debate about when different levels are accessed during spoken word recognition. Under serial processing models, comprehension is sequential. In contrast, under parallel processing models, simultaneous activation of representations at multiple levels can occur. The current study investigates this issue by isolating neural responses to syntactic class distinctions from acoustic and phonological responses. EEG data were collected in an event-related potential (ERP) experiment in which participants (N = 26) listened to words varying in syntactic class (nouns versus adjectives) that were controlled for low-level acoustic differences via cross-splicing. Machine learning techniques were used to decode syntactic class from ERP responses over time. Results showed that syntactic class is decodable approximately 160–190 ms after the average syntactic point of disambiguation in the words, during which listeners are still processing acoustic information. This supports the prediction that different levels of representation have overlapping timecourses. Overall, these results are consistent with a parallel, interactive processing model of spoken word recognition, in which higher-level information—such as syntactic class—is accessed while acoustic analysis is still occurring.

Тематическая сфера «Компьютерные технологии» / “Computer Technologies” на системно-языковом уровне по материалам неспециальных словарей русского и английского языков

The aim of the study is to determine the features of the semantic structure of the thematic sphere “Computer Technologies” in the Russian and English languages. The article highlights the core and peripheral semes of the thematic sphere under study and describes the principles of grouping lexical units within the boundaries of one thematic sphere according to non-specialized dictionaries. The scientific novelty lies in the fact that for the first time such a large lexical grouping of a language as a thematic sphere is structured at different levels of representation, in particular, at the system-linguistic level with a description of the core components. As a result, a structural-semantic field model of the thematic sphere “Computer Technologies” is presented using the methods of component and context analysis.

Summarizing source code with hierarchical code representation

Code summarization aims to automatically generate natural language descriptions for code, and has become a rapidly expanding research area. Data-driven code summarization models based on neural networks have proliferated in recent few years. Almost all of existing neural models are built upon the granularity of token or AST node. This has several drawbacks: a) Code summarization requires high-level knowledge of code while token representations are limited to provide a global view; b) Such approaches can hardly model the hierarchy of code; c) Long input codes challenge such models to handle long-range dependencies due to the large number of tokens and AST nodes. To address these issues, we propose a novel framework to utilize hierarchical representation of code to generate better summaries. We consider two levels of code hierarchy: token-level and statement-level. Our framework contains a pair of customized encoder-decoder models for tokens and AST of code respectively. Each of them has a hierarchical encoder that aims to extract both token and statement-level code features, and an attentional decoder with the ability to attend to those different levels of representation during decoding. They are then combined to predict summaries via ensemble learning. We conduct extensive experiments to evaluate our models on a large Java corpus. The experimental results show that our approach outperforms several state-of-the-art baselines by a substantial margin. In conclusion, our approach could better learn global information of code and shift attention between important statements during summary generation. With the help of hierarchical attention, the models are able to locate keywords more accurately in a top-down way. Ensemble learning is also proved to be an effective way to benefit from multiple input sources.

“I’m Not a Good Drug Dealer”: Styles of Buprenorphine Diversion in a Multisite Qualitative Study

Background Against the backdrop of the U.S. opioid epidemic, there has been a concerted movement to improve access to buprenorphine maintenance therapy (BMT). In Pennsylvania, where overdose mortality increased 65% between 2015 and 2017, over $75 million has been appropriated toward BMT since 2016. Concurrently, efforts to increase BMT availability while lowering barriers to entry have given way to fears of increased diversion and illegitimate patients. Little is known about the circumstances and motivations that surround buprenorphine diversion, particularly within the context of treatment expansion. Method Drawing on 27 in-depth interviews with individuals who reported sharing or selling buprenorphine in the past year, in this study we consider the relationship between treatment access, treatment experiences, and individuals’ decision to divert buprenorphine, while further comparing motivations for buprenorphine diversion across two Pennsylvania counties with disparate levels of BMT availability. Results We identify four styles of buprenorphine diversion (“ad hoc sellers,” “concerned suppliers,” “social sharers,” “professional dealers”), with different levels of representation by county. Overall, our analysis found the explicit economic exploitation of BMT was rare, while a plurality of participants reported selling unwanted or unneeded buprenorphine only when presented with an opportunity. Conclusions Across our typology, market demand in the form of unmet need for buprenorphine was the major driver of diversion, suggesting that “supply-side interventions” intended to again limit access to BMT may be counterproductive.

Metacognitive asymmetries in visual perception.

People have better metacognitive sensitivity for decisions about the presence compared to the absence of objects. However, it is not only objects themselves that can be present or absent, but also parts of objects and other visual features. Asymmetries in visual search indicate that a disadvantage for representing absence may operate at these levels as well. Furthermore, a processing advantage for surprising signals suggests that a presence/absence asymmetry may be explained by absence being passively represented as a default state, and presence as a default-violating surprise. It is unknown whether the metacognitive asymmetry for judgments about presence and absence extends to these different levels of representation (object, feature, and default violation). To address this question and test for a link between the representation of absence and default reasoning more generally, here we measure metacognitive sensitivity for discrimination judgments between stimuli that are identical except for the presence or absence of a distinguishing feature, and for stimuli that differ in their compliance with an expected default state.

Natural Language Processing and Computational Linguistics

Natural Language Processing and Computational Linguistics

A corpus-based study on contrast and concessivity of the connective ‑cimanin Korean

AbstractProviding a corpus-based analysis of the contrastive connective -cimanin Korean, this study demonstrates that global connections are as frequent as local connections as opposed to previous literature. In representing various senses possible with a -cimanconnection, this study adopts a fuzzy representation, where meanings range from conceptual to discoursal. The identified meanings include explicit contrast, denial of expectation, speech act hedges and idiomatic expressions. The fuzzy representation is supported for at least two reasons. First, categorization of some cases is often blurred. Second, it can better capture the relatedness of various meanings whose enduring sense concerns ‘contrast’. It is further revealed that interpretations of-cimanphrases are compositionally made with co-occurring linguistic units. The “pragmatic” meanings of-cimanconstructions are explained in terms of different levels of representations in which the contrast occurs. We further investigate the possibility of-cimanas a stance/discourse marker with accompanying expressions.

Designing a three levels poster to visualize chemical reactions

Activities that engage high school students in visualizing chemical phenomena can help them develop representational competence to think about and communicate their understanding of these phenomena. This paper presents a collaborative learning activity that can engage precollege students in designing a poster that describes four different types of chemical reactions at the Macro, Micro, and Symbolic levels of representation. In this activity, students will work in groups to create the poster and identify the connections between the different levels of representations in order to understand and explain the mechanisms underlying the different types of chemical reactions. The activity can be performed in in-person or virtual classrooms.

Partial awareness can be induced by independent cognitive access to different spatial frequencies

Partial awareness—an intermediate state between complete consciousness and unconsciousness—has been explained by independent cognitive access to different levels of representation in hierarchical visual processing. This account, however, cannot explain graded visual experiences in low levels. We aimed to explain partial awareness in low levels of visual processing by independent cognitive access to different spatial frequencies. To observe partial awareness stably, we used a novel method. Stimuli were presented briefly (12 ms) and repeatedly with a specific inter-stimulus interval, ranging from 0 to 235 ms. By using various stimuli containing high and low spatial frequencies (superimposed sinusoidal gratings, Navon letters, and scenes), we found that conscious percept was degraded with increasing inter-stimulus intervals. However, the degree of degradation was smaller for low spatial frequency than for high spatial frequency information. Our results reveal that cognitive access to different spatial frequencies can occur independently and this can explain partial awareness in low levels of visual processing.

The origin of misconceptions in inorganic chemistry and their correction by computer modelling

The paper goal was to analyse the typical mistakes in the learning of the university course of inorganic chemistry, determine the origin of misconceptions and estimate the effectiveness of the use of computer simulations to correct false chemical concepts. Ten problems that are the most typical for students were revealed. One of the leading causes of their occurrence is the failure of many students to form mental relationships between different levels of representation of chemical knowledge - microscopic, macroscopic and symbolic. Other reasons include an insufficient understanding of the material, the incompleteness of the knowledge of the microscopic basis of processes, and inability to work with different models, including misunderstanding of their purpose and constraints. NetLogo programming environment was used for students’ self-administering tests to study gas laws. Scope for the usage of NetLogo models was estimated in correcting of incorrectly formed conceptions of the chemical knowledge. Independent work with NetLogo models facilitates the formation of stable relationships between multiple levels of representation of chemical information. It improves an understanding of the studied topic fundamentally, and this holds for all students practically independently of their grounding level in chemistry. The introduction of computer simulation into the practice of teaching chemical subjects shows promise. Still, it requires the solution of several scientific, methodological, logistical and organisational issues.