Impact Of Prior Knowledge Research Articles

Prior knowledge of a calculus course: The impact of prior knowledge on students’ errors

We conducted a descriptive study among first-year engineering students at the Anton de Kom University of Suriname. We analyzed students’ errors regarding necessary prior knowledge in a calculus A exam. We found that the stage of the solution in which prior knowledge is required impacts the importance of prior knowledge. We also found that many errors concerned basic algebra and trigonometry concepts and skills. We concluded that even though the required prior knowledge concerns basic algebra and trigonometry, the stage of the solution in which prior knowledge is needed is of great importance.

Multi-Armed Bandit Algorithms in A/B Testing: Comparing the Performance of Various Multi-Armed Bandit Algorithms in the Context of A/B Testing

A/B testing is a widely used technique for comparing the effectiveness of different versions of a product or service. Multi-armed bandit algorithms have emerged as a promising approach to optimize A/B testing by dynamically allocating traffic to the best-performing variant. This paper provides an in-depth comparison of the performance of various multi-armed bandit algorithms in the context of A/B testing. We evaluate the algorithms based on their ability to maximize rewards, minimize regret, and adapt to changing environments. The findings highlight the strengths and limitations of each algorithm and guide the selection of the most suitable algorithm for different A/B testing scenarios. We also discuss the trade-offs between exploration and exploitation, the impact of prior knowledge, and the scalability of multi-armed bandit algorithms in large-scale A/B testing. The paper concludes with recommendations for future research directions and practical implications for implementing multi-armed bandit algorithms in A/B testing

The impact of prior knowledge on causal structure learning

Causal Bayesian networks have become a powerful technology for reasoning under uncertainty in areas that require transparency and explainability, by relying on causal assumptions that enable us to simulate hypothetical interventions. The graphical structure of such models can be estimated by structure learning algorithms, domain knowledge, or a combination of both. Various knowledge approaches have been proposed in the literature that enables us to specify prior knowledge that constrains or guides these algorithms. This paper introduces some novel, and also describes some existing, knowledge-based approaches that enable us to combine structure learning with knowledge obtained from heterogeneous sources. We investigate the impact of these approaches on structure learning across different algorithms, case studies and settings that we might encounter in practice. Each approach is assessed in terms of effectiveness and efficiency, including graphical accuracy, model fitting, complexity, and runtime; making this the first paper that provides a comparative evaluation of a wide range of knowledge approaches for structure learning. Because the value of knowledge depends on what data are available, we illustrate the results both with limited and big data. While the overall results show that knowledge becomes less important with big data due to higher learning accuracy rendering knowledge less important, some of the knowledge approaches are found to be more important with big data. Amongst the main conclusions is the observation that reduced search space obtained from knowledge does not always imply reduced computational complexity, perhaps because the relationships implied by the data and knowledge are in tension.

Impact of Prior Knowledge about Autonomous Vehicles on the Public Attitude

It is anticipated that AVs will offer multiple benefits, such as an improvement in the level of mobility, increasing the level of comfort, and reducing the number of traffic accidents. However, the public attitude is the main determinant factor that will face the deployment of AVs and in turn affect their implications. Over the last few years, there was a debate on the impact of the level of knowledge about AVs on public attitudes. While some studies show that people with higher levels of knowledge about AVs are the most optimistic, some other studies show that the public attitude moves in the negative with an increase in the level of knowledge. Thus, this study focuses exclusively on quantifying and understanding the impact of the level of knowledge and the public attitude in the US. A questionnaire survey was designed and conducted between June and November, 2022. A total of 5778 complete responses were received from all over the US and the analysis was conducted to estimate the public attitude and level of knowledge by region. The results show that there is a negative shift in public attitude with the increase in the level of knowledge about AVs. In addition, the results show that 1% increase in the level of knowledge about AVs is associated with 0.65%, 0.68%, and 2466 (USD) $ decrease in the level of interest, trust, and willingness to pay for AV and 0.56% increase in the level of concern about traveling in AVs. Moreover, the results are discussed in light of both the diffusion of innovation theory and the Gartner Hype curve. Doi: 10.28991/CEJ-2023-09-04-017 Full Text: PDF

How attention and knowledge modulate memory: The differential impact of cognitive conflicts on subsequent memory—A review of a decade of research

In order to cope with cognitive conflicts, attention and knowledge are required. In some conditions, cognitive conflicts can boost subsequent memory and in other conditions, they can attenuate subsequent memory. The goal of the present study is to provide a narrative review of studies from the last decade in which Stroop or flanker conflicts, task switching, perceptual disfluency or semantic incongruence were manipulated at study. We propose an integrative framework considering attentional mechanisms and knowledge structures. Attentional mechanisms can refer to conflict resolution, which is required to explain the memory benefit for incongruent stimuli in Stroop and Flanker paradigms. Attentional mechanisms can also refer to attention allocation, which is required to explain the memory cost for targets and the memory benefit for task-irrelevant distractors in task-switching paradigms. Moreover, attention allocation policies can also account for the inconsistent results for perceptual disfluency manipulations. Prior knowledge is required to explain effects of semantic congruency and incongruency: Information that is expected, or congruent with prior knowledge, is better remembered, namely by pre-existing schemata. Moreover, information that is unexpected or incongruent with prior knowledge attracts attention and is better remembered. The impact of prior knowledge on memory performance thus results in a U-shape function. We integrate the findings according to this framework and suggest directions for future research.

Impact of Prior Knowledge in English to Follow Degree in English Medium - Special Reference to Computer Science Students

Impact of Prior Knowledge in English to Follow Degree in English Medium - Special Reference to Computer Science Students

Adolescents' Sexual and Reproductive Health Behavior Amidst the Covid-19 Pandemic in Lurambi Sub-County, Kakamega, Kenya: The Impact of Prior Knowledge

Adolescents' Sexual and Reproductive Health Behavior Amidst the Covid-19 Pandemic in Lurambi Sub-County, Kakamega, Kenya: The Impact of Prior Knowledge

Impact of prior knowledge and psychological distance on tourist imagination of a promoted tourism event

Experiential marketing has emerged as an effective strategy to promote hedonic tourism and hospitality experiences through stimulating tourist imagination and positive emotions. This study aims to broaden the existing literature by exploring potential imagination facilitator and/or inhibitor in the context of a local tourism event, “The White Party” on the Gold Coast, with a sample of 655 participants. Structural equation model (SEM) using SmartPLS software and cluster analysis using SPSS were conducted to test research hypothesis and identify three audience groups (event dreamers, indecisive audience, and uninterested audience). Research outcomes demonstrate that video is more effective than poster in evoking tourist imagination and positive emotions, leading to stronger intentions to attend the promoted event. More importantly, prior knowledge is found as an imagination facilitator while psychological distance plays the role of an imagination inhibitor. This study provide valuable insights into how the effectiveness of experiential marketing varies depending on the audience characteristics. Therefore, event managers can optimize the effectiveness of experiential marketing by (1) targeting more receptive audience groups (i.e., event dreamers and indecisive audience); (2) adding more “psychologically close” details of the promoted event (e.g., similar age or cultural groups) and (3) enhancing message repetition.

Patient perceptions of trust formation in the surgeon-patient relationship: A thematic analysis

ObjectiveTrust is crucial to the success of any personal or professional relationship. Literature on trust in the surgeon-patient relationship has been largely explored through quantitative methodologies, primarily examining why trust may or may not exist. We aimed to qualitatively elucidate the mechanisms of how trust develops between otolaryngologists and their patients. MethodsPatients were recruited by surgery scheduling staff following an outpatient visit where a decision had been made to proceed with surgery at a tertiary academic medical center. We used qualitative realist thematic analysis of phone interviews to explore participants’ (n = 17) perceptions and conceptualization of trust formation within the surgeon-patient relationship. ResultsThematic analysis revealed three themes regarding trust formation in the surgeon-patient relationship: 1) Trust Across Various Contexts; 2) Impact of Prior Knowledge; and 3) Interpersonal Connection during the Clinical Encounter. ConclusionAn interpersonal surgeon-patient connection is crucial to the formation of trust. Trust is also influenced by surgeon and institutional reputation and witnessed surgeon interactions with the healthcare team. Practice ImplicationsPatients perceive trust in a surgeon as carrying unique importance. To optimize conditions for trust development in this context, surgical care paradigms should promote meaningful preoperative interpersonal surgeon-patient relationships and positive surgeon and institutional reputations.

Impact of prior knowledge on privacy leakage in trajectory data publishing

The rapid growth in the usage of location-based services has resulted in extensive research on users’ trajectory data publishing. But, a key concern here is a potential breach of user privacy through various linkage attacks by an efficient adversary. There exist a few privacy preservation methods to defend against either single or combination of linkage attacks, namely Identity linkage attack, Attribute linkage attack, and Similarity attack. However, the Correlated-records linkage attack has not been studied in any previous privacy preservation methods, and there is no privacy preservation method to address all the above four linkage attacks. In this paper, a novel anonymization method is proposed to provide the privacy guarantee to users against all the four linkage attacks. The proposed method consists of two phases, namely virtualization and suppression. The virtualization method works as a replacement mechanism for the sensitive attribute and the suppression method works as anonymization mechanism for users trajectories, in order to anonymize the trajectory datasets for preserving users’ privacy from the above four linkage attacks. To validate the efficiency of the proposed method, it is also compared with existing methods, namely KCL-L, KCL-G and KCL-PPTD, considering both synthetic and real-time datasets. The experimental results exhibit that the proposed approach results in better performance with a significant reduction in the information loss when compared to other states of the art methods.

Working Memory Capacity but Not Prior Knowledge Impact on Readers' Attention and Text Comprehension

Reading digital texts is a common practice in today’s education. Prior studies showed that the coherence of a text can influence text comprehensibility with low degrees of coherence causing attention failures (mind wandering) and, consequently, negatively impacts reading comprehension. In addition, working memory capacity (WMC) and prior knowledge of the subject have been suggested to be related to both reading comprehension and mind wandering. However, results remain controversial as the interaction of these three factors has not yet been explored. 90 participants either studying law or a different subject read either a coherent or incoherent version of the same unfamiliar hypertext about the copyright law. While reading, they reported self-caught mind wandering with task-embedded thought probes. After reading the hypertext, participants were tested on their text comprehension. Supporting prior findings, mind wandering did occur more frequently when participants read difficult rather than easy texts regardless of their undergraduate course. Moreover, this was modulated by WMC in that participants with lower WMC exhibited more frequent mind wandering than high WMC participants solely when reading low coherent texts. In addition, high WMC participants outperformed low WMC participants on all measures of text comprehension. With a low WMC it seems difficult to inhibit irrelevant information and access related information from working memory, especially when text complexity is high. Interestingly, the present results also indicate that prior knowledge benefits later text comprehension despite not affecting reader’s attention. These findings provide insights into processing attention during reading online texts.

Impact of Prior Knowledge and Data Correlation on Privacy Leakage: A Unified Analysis

It has been widely understood that differential privacy (DP) can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate that this may not be true for correlated data, and indicate that three factors could influence privacy leakage: the data correlation pattern, prior knowledge of adversaries, and sensitivity of the query function. This poses a fundamental problem: what is the mathematical relationship between the three factors and privacy leakage? In this paper, we present a unified analysis of this problem. A new privacy definition, named \textit{prior differential privacy (PDP)}, is proposed to evaluate privacy leakage considering the exact prior knowledge possessed by the adversary. We use two models, the weighted hierarchical graph (WHG) and the multivariate Gaussian model to analyze discrete and continuous data, respectively. We demonstrate that positive, negative, and hybrid correlations have distinct impacts on privacy leakage. Considering general correlations, a closed-form expression of privacy leakage is derived for continuous data, and a chain rule is presented for discrete data. Our results are valid for general linear queries, including count, sum, mean, and histogram. Numerical experiments are presented to verify our theoretical analysis.

Age-Related Differences in the Impact of Prior Knowledge on Recognition Performance: A Face Recognition Study

ABSTRACTBackground/Study context: The dual-process hypothesis of memory and aging (DPHMA) postulates a decline of recollection with no decline in recognition. While the age-related recollection deficit is well-documented, any age-changes in the familiarity process remain unclear. Some studies have shown that familiar and meaningful material can enhance the recognition performance of older adults. The goal of the present study was to explore the impact of familiar material on age-related recognition-memory decline, using a dual-process approach.Methods: One hundred participants (50 young adults and 50 older adults) performed two recognition tasks using an unfamiliar and a famous face recognition task. A receiver operating characteristic (ROC) analysis was used to examine the memory processes underlying recognition memory.Results: The older adults showed lower performance on recognition accuracy and false alarms rate, only in the unfamiliar-face recognition task. A DPHMA-like pattern on unfamiliar-face recognition task was obtained. Prior knowledge in the famous-face recognition task improved recollection for older adults and no age-related deficit was found.Conclusion: The present study suggests that episodic memory deficits in healthy aging are primarily driven by a recollection deficit while familiarity is relatively well spared. However, this age-related recollection deficit could be alleviated using knowledge-based material.

Impact of Prior Knowledge of Engineering Materials by Students of Metalwork Technology Education for Effective Practical Work

Impact of Prior Knowledge of Engineering Materials by Students of Metalwork Technology Education for Effective Practical Work

An introduction to Bayesian statistics in health psychology

ABSTRACTThe aim of the current article is to provide a brief introduction to Bayesian statistics within the field of health psychology. Bayesian methods are increasing in prevalence in applied fields, and they have been shown in simulation research to improve the estimation accuracy of structural equation models, latent growth curve (and mixture) models, and hierarchical linear models. Likewise, Bayesian methods can be used with small sample sizes since they do not rely on large sample theory. In this article, we discuss several important components of Bayesian statistics as they relate to health-based inquiries. We discuss the incorporation and impact of prior knowledge into the estimation process and the different components of the analysis that should be reported in an article. We present an example implementing Bayesian estimation in the context of blood pressure changes after participants experienced an acute stressor. We conclude with final thoughts on the implementation of Bayesian statistics in health psychology, including suggestions for reviewing Bayesian manuscripts and grant proposals. We have also included an extensive amount of online supplementary material to complement the content presented here, including Bayesian examples using many different software programmes and an extensive sensitivity analysis examining the impact of priors.

The impact of prior knowledge from participant instructions in a mock crime P300 Concealed Information Test

In P300-Concealed Information Tests used with mock crime scenarios, the amount of detail revealed to a participant prior to the commission of the mock crime can have a serious impact on a study's validity. We predicted that exposure to crime details through instructions would bias detection rates toward enhanced sensitivity. In a 2×2 factorial design, participants were either informed (through mock crime instructions) or naïve as to the identity of a to-be-stolen item, and then either committed (guilty) or did not commit (innocent) the crime. Results showed that prior knowledge of the stolen item was sufficient to cause 69% of innocent–informed participants to be incorrectly classified as guilty. Further, we found a trend toward enhanced detection rate for guilty–informed participants over guilty–naïve participants. Results suggest that revealing details to participants through instructions biases detection rates in the P300-CIT toward enhanced sensitivity.

The Impacts of Virtual Manipulatives and Prior Knowledge on Geometry Learning Performance in Junior High School

Previous studies on the effects of virtual and physical manipulatives have failed to consider the impact of prior knowledge on the efficacy of manipulatives. This study focuses on the learning of plane geometry in junior high schools, including the sum of interior angles in polygons, the sum of exterior angles in polygons, and the properties of parallel lines. This study adopted a quasi-experimental design of pre-test and post-tests with nonequivalent groups. The participants comprised four classes in the 8th grade. Students were randomly divided into two groups: the virtual manipulatives group and the physical manipulatives group. Two-way factorial analysis of covariance was adopted to compare the impact of manipulatives (virtual vs. physical) and prior knowledge (high vs. low) on learning outcomes and attitudes toward mathematics. Our results demonstrated that students with high prior knowledge using virtual manipulatives had better posttest performance than did the physical manipulatives group, and reported taking greater enjoyment in mathematics. These students also perceived the importance of mathematics more strongly than those with low prior knowledge. Students with high prior knowledge also presented stronger motivation to study mathematics and freedom from fear of the subject than those with low prior knowledge.

IMPACT OF ACTIVITY-BASED MATHEMATICS INSTRUCTION ON STUDENTS WITH DIFFERENT PRIOR KNOWLEDGE AND READING ABILITIES

The aim of this paper is to examine the impact of activity-based mathematics instruction on mathematics performance and investigate those factors, which contribute to the mathematics performance of a sample of children aged between 10 and 12 years. The study was designed to consider the impact of prior knowledge, self-regulation, prior attitude, and reading ability on students’ mathematics performance. The data were collected by surveying the students using four instruments: achievement, attitude toward mathematics, self-regulation, and reading ability. The initial results indicated that activity-based mathematics instruction made a significant contribution to students’ mathematics performance and mathematics attitude. Students with high prior knowledge and high self-regulation skills demonstrated greater gains in mathematics performance. Students with high prior knowledge also showed a more positive attitude toward mathematics. However, students with low prior knowledge and low reading skills showed the greatest gains in mathematics performance as measured by a retention test. Recent perspectives on the effect of activity-based instruction and the predictors of mathematics performance were described, and recommendations were offered.

The Impact of Prior Knowledge about Visual Feedback on Motor Performance and Learning

Performers adopt strategies to use visual information if they know that it will be available whereas uncertainty about its availability leads performers to prepare for the worst-case scenario. Since the impact of prior knowledge has generally been examined as performance-based changes across a series of trials, this study investigates the impact of prior knowledge on learning. Participants practiced target-directed aiming movements either with cues about the random availability of vision, no cues regarding vision or in blocks of stable visual information availability. Participants who received prior knowledge were more efficient in preparing their acquisition movements when vision was available. In retention and transfer, all participants were able to take advantage of the visual information available in order to optimize performance outcome. Thus, adult performers appear able to change their strategic behavior quickly to accommodate new sensory and prior knowledge conditions.

Reconstructing gene-regulatory networks from time series, knock-out data, and prior knowledge

BackgroundCellular processes are controlled by gene-regulatory networks. Several computational methods are currently used to learn the structure of gene-regulatory networks from data. This study focusses on time series gene expression and gene knock-out data in order to identify the underlying network structure. We compare the performance of different network reconstruction methods using synthetic data generated from an ensemble of reference networks. Data requirements as well as optimal experiments for the reconstruction of gene-regulatory networks are investigated. Additionally, the impact of prior knowledge on network reconstruction as well as the effect of unobserved cellular processes is studied.ResultsWe identify linear Gaussian dynamic Bayesian networks and variable selection based on F-statistics as suitable methods for the reconstruction of gene-regulatory networks from time series data. Commonly used discrete dynamic Bayesian networks perform inferior and this result can be attributed to the inevitable information loss by discretization of expression data. It is shown that short time series generated under transcription factor knock-out are optimal experiments in order to reveal the structure of gene regulatory networks. Relative to the level of observational noise, we give estimates for the required amount of gene expression data in order to accurately reconstruct gene-regulatory networks. The benefit of using of prior knowledge within a Bayesian learning framework is found to be limited to conditions of small gene expression data size. Unobserved processes, like protein-protein interactions, induce dependencies between gene expression levels similar to direct transcriptional regulation. We show that these dependencies cannot be distinguished from transcription factor mediated gene regulation on the basis of gene expression data alone.ConclusionCurrently available data size and data quality make the reconstruction of gene networks from gene expression data a challenge. In this study, we identify an optimal type of experiment, requirements on the gene expression data quality and size as well as appropriate reconstruction methods in order to reverse engineer gene regulatory networks from time series data.