Influence Of Prior Knowledge Research Articles

Prior knowledge and new learning: An experimental study of domain-specific knowledge.

It is commonly claimed that higher domain knowledge enhances new learning-the knowledge-is-power hypothesis. However, a recent meta-analysis (Simonsmeier et al., 2022) has challenged this idea, finding no overall relationship between prior knowledge and new learning across hundreds of highly variable effect sizes. The authors note that this variability and lack of randomized controlled experiments preclude broad claims regarding the influence of prior knowledge on learning. The present study (conducted in 2020) provides an experimental assessment of the causal effect of prior domain knowledge on new learning. Participants were randomly assigned to receive training in one of two academic domains over 3 days before learning new information about topics in both domains for a later test. Training was specific to three of four topics within that domain, allowing the untrained topic in the trained domain to act as a measure of new learning in that domain. New learning, measured as final test performance or knowledge gains, did not differ between the high and low domain knowledge conditions. Experimentally induced prior domain knowledge did not affect new learning. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

An In-Depth Evaluation of Educational Burst Games in Relation to Learner Proficiency

Game-based learning assessments rely on educational data mining approaches such as stealth assessments and quasi mixed methods that help gather data on student learning proficiency. Rarely do we see approaches where student proficiency in learning is woven into the game’s design. Educational burst games (EBGs) represent a new approach to improving learning proficiency by designing fast-paced, short, repetitive, and skill-based games. They have the potential to be effective learning interventions both during instruction in the classroom and during after-school activities such as assignments and homework. Over five years, we have developed two EBGs aimed at improving linear algebra concepts among undergraduate students. In this study, we provide the results of an in-depth evaluation of the two EBGs developed with 45 participants that represent our target population. We discuss the role of EBGs and their design constructs, such as pace and repetition, the effect of the format (2D vs. 3D), the complexity of the levels, and the influence of prior knowledge on the learning outcomes.

Interactive influences of prior knowledge on episodic memory

Interactive influences of prior knowledge on episodic memory

The influence of prior knowledge on the self-efficacy of students enrolled in a tertiary introductory accounting course

ABSTRACT Given that self-efficacy has been found to influence the achievement of learning outcomes, this study examines the influence of prior knowledge on the self-efficacy levels of students enrolled in a tertiary level introductory accounting course. The self-efficacy levels of 272 students, captured through an in-class survey, were compared by gender, age, prior knowledge and work experience. The results suggest there was no significant difference in self-efficacy levels between students who had previously studied accounting at secondary school and those who had not. This research makes a theoretical contribution by confirming that prior knowledge does not necessarily influence an individual’s level of self-efficacy when there are few similarities between the prior knowledge gained and the current course being studied. In addition, there is a practical contribution which suggests that students who have not studied accounting at secondary school should not be discouraged from studying accounting at tertiary level.

An Investigation on the Effect of Prior Knowledge on Farmers' Decisions to Use Computers in Agriculture: Empirical Evidence from Rural Punjab, Pakistan

This study deeply analyzes the complex relationship between internal factors, such as farmers' existing computer knowledge, and adoption within the context of Pakistan's agricultural sector. To collect the data from a random sample of 416 farmers drawn from Punjab Province's five agro-ecological zones, surveys were conducted on computer knowledge and adoption intentions. The results would then reveal that although farmers still enjoy the benefits associated with computers, they actually require better skills on some specified hardware and related services in order to conduct prime activities such as assessing e-portals, online market search, and keeping financial, livestock, business planning, and payroll records. Specifically, the study outcomes go beyond those of the conventional adoption models since it shows that behavioral intentions could directly formed by a influence of prior knowledge. The study points to targeted interventions by extension professionals and policymakers towards preparing the farmers with all the computer skills required in their work, promoting digital inclusion towards the eventual development of agriculture.

The Effects of the Medium of Notetaking on the Delayed Learning Effect of College Students: A Mediated Moderation Model.

This study systematically probed the relationship between the medium of taking classroom notes (virtual variable, electronic notetaking = 0 vs. traditional notetaking = 1), the word count in each medium, as well as the review process, and the students' delayed learning effect for each notetaking approach. Data were collected from 189 college students, with the influence of gender and prior knowledge being controlled. The conclusions were as follows. (1) The notetaking medium was positively correlated with delayed test scores, irrespective of whether reviews were allowed or not. (2) The mediating role of word count between notetaking medium and delayed test scores was moderated by review. That is, when reviews were allowed, a significant correlation was found between the medium of the notes and the delayed test scores; when reviews were not allowed, the mediating effect of word count was not significant.

A second chance for first impressions: evidence for altered impression updating in borderline personality disorder

BackgroundIndividuals with borderline personality disorder (BPD) frequently alter between idealizing and devaluing other persons, which has been linked to an increased tendency to update self-relevant beliefs and impressions. We hypothesized that increased impression updating could stem from reduced attitude contextualization, i.e., a process in which impression-disconfirming information is linked to contextual cues.MethodsIndividuals diagnosed with BPD and controls (recruited online, with unknown diagnostic status) completed an impression formation paradigm. They first learned about the positive or negative behaviors of others in one Context A (e.g., Person 1 is helpful), followed by learning about behaviors of the opposite valence in a second Context B (Person 1 is rude). We also manipulated between participants whether the observed behaviors were directed toward the study participants (self-relevant) or, more generally, at other people (other-relevant). The contexts were marked by differently-colored backgrounds (e.g., yellow vs. blue), to avoid influences of prior knowledge or experiences. After exposure to information in both contexts, participants rated their impressions of the persons in Context A, Context B, and, crucially, a previously unknown Context C (white background). We examined whether the initial or an updated impression (re-)emerged in Context C.ResultsInitial impressions remained stable and dominated the ratings of controls across contexts A, B, and C for both self-relevant and other-relevant behaviors, consistent with contextualizing impression-disconfirming information. As expected, however, individuals with BPD only showed updated impression ratings in Context C for self-relevant behaviors, consistent with the assumed reduced tendency to contextualize impression-disconfirming self-relevant information. Further exploratory analyses suggest that more severe BPD symptoms predicted more pronounced impression updating in the self-relevant condition.ConclusionsThe findings help to illuminate the mechanisms underlying interpersonal problems in individuals with BPD. People with BPD are not just more inclined to discard positive first impressions but to re-evaluate disliked others when they behave positively, contributing to the volatility of interactions with others. Contextualization has known and modifiable antecedents, and the study may thus provide potential targets for therapeutic intervention. Future studies will need to replicate the findings with specified controls.

To update or to create? The influence of novelty and prior knowledge on memory networks.

Schemas are foundational mental structures shaped by experience. They influence behaviour, guide the encoding of new memories and are shaped by associated information. The adaptability of memory schemas facilitates the integration of new information that aligns with existing knowledge structures. First, we discuss how novel information consistent with an existing schema can be swiftly assimilated when presented. This cognitive updating is facilitated by the interaction between the hippocampus and the prefrontal cortex. Second, when novel information is inconsistent with the schema, it likely engages the hippocampus to encode the information as part of an episodic memory trace. Third, novelty may enhance hippocampal dopamine through either the locus coeruleus or ventral tegmental area pathways, with the pathway involved potentially depending on the type of novelty encountered. We propose a gradient theory of schema and novelty to elucidate the neural processes by which schema updating or novel memory traces are formed. It is likely that experiences vary along a familiarity-novelty continuum, and the degree to which new experiences are increasingly novel will guide whether memory for a new experience either integrates into an existing schema or prompts the creation of a new cognitive framework. This article is part of the theme issue 'Long-term potentiation: 50 years on'.

Application of Cognitive Load Theory in VR Development and Its Impact on Learning: A Perspective on Prior Knowledge, Learning Interest, Engagement, and Content Comprehension

This research examines the utilization of Virtual Reality (VR) and its implications for the learning process, specifically focusing on learning interest, prior knowledge, learning engagement, and content comprehension. The central objective is to establish a comprehensive model that unravels the intricate interplay between these factors within the context of VR-based learning. The study also aims to shed light on the impact of integrating Cognitive Load Theory into VR development and its effects on the learning experience. Adopting an observational design, this study elucidates the intricate relationships among learning interest, prior knowledge, learning engagement, and content comprehension in VR-based education. The VR technology employed in this research has previously undergone rigorous feasibility testing. The VR application was designed following cognitive load theory principles. Its immersive content offers users a lifelike immersion into the natural habitats of diverse animal species across various global regions. By leveraging VR technology, elementary school students engage in a more profound and authentic learning journey. A total of 85 participants, encompassing fourth and fifth-grade elementary school students, were involved in the study. These students were drawn from schools situated in rural areas in particular regions in Indonesia and had moderate to low economic backgrounds. The variables under examination include Prior Knowledge, Learning Interest, Engagement, and Content Comprehension as learning outcomes. Data analysis was conducted utilizing a blend of linear regression and path analysis techniques, with a confidence level of 95%. The Guttman scale questionnaire was used, and total scores were transformed into a ratio scale through a conversion process. The study reveals a positive correlation between learning interest and learning outcomes, highlighting that a strong interest in a subject contributes to improved learning results. Additionally, both learning interest and prior knowledge influence learning engagement. Students with higher learning interests and prior knowledge are more likely to actively engage in the learning process actively, underscoring internal factors' role in motivating participation. Learning engagement moderates the relationships between learning interest, prior knowledge, and learning outcomes. By enhancing the effect of learning interest and prior knowledge on learning outcomes, engagement enables more comprehensive and practical information processing.

Perceptions and knowledge of ecosystem services in urban river systems, Eastern Cape, South Africa

The ecosystem services concept has been studied in rural contexts but not as much in urban landscapes, particularly for aquatic ecosystems in Africa. There is little knowledge of how people perceive ecosystem services (ES) supplied by rivers in urban centres and the factors influencing their perceptions. To fill this gap, the research presented here addresses two objectives: 1) to assess local communities’ perception and knowledge of different types of ecosystem services of an urban river in South Africa, 2) to assess the influence of particular demographic characteristics (age and gender), socio-economic characteristics (education, housing type, income, and years of residence), and prior knowledge of the ecosystem services concept on people’s perception of the ecosystem services provided by the Swartkops River in the Eastern Cape of South Africa. To address these objectives, a team of ten trained field staff collected 181 surveys between May–June 2021 in communities within the river catchment. We used multinomial logit models to analyse the relationships between demographic and socio-economic variables, and people’s perception of ecosystem services. For the majority of the ecosystem services, there was almost an equal split between the perception “sometimes” and “never” that the river provides a particular ecosystem service, and people were less likely to say that the river always provide a particular service. Prior knowledge of the ecosystem services concept, years of residence, age, income and housing type significantly influence people’s perception of ecosystem services. Prior knowledge, was important for the intangible services, whereas years of residence was more important for tangible services with direct use value. Level of formal education was not a significant variable. An important implication of this study is that ecological interventions can be designed through an understanding of factors influencing people’s perceptions of ecosystem services.

From perception to performance: To establish the level of influence of prior knowledge on the scores of the reading section of the International English Language Testing System (IELTS) of B1 level students of a public university in Ecuador

The general objective of this article is to determine whether the perceptions of B1 level students at a public university in Ecuador about their prior knowledge influence their scores in the reading section of the International English Language Testing System (IELTS). The type of research was mixed, since numerical data were collected quantitatively and then generalized, and through a qualitative study important aspects were examined regarding the students' prior knowledge and whether it influences their performance in the aforementioned international test. For the sample, 28 students were selected, which were approached by two key instruments for the study, such as: the simulated test and the survey. The results obtained showed that the students' perceptions of their prior knowledge do not correlate with their performance in the reading section of the International English Language Testing System (IELTS). Also, there was a high incidence of students who felt that reading does not help much in learning about a topic, however, they think it is of great help for vocabulary improvement.

Analisis of Influence of prior Knowledge on the Scores of the Reading Section of the International English Language Testing System (IELTS) B1 level

The general objective of this article is to establish from a theoretical approach the level of influence of prior knowledge in the scores of the reading section of the International English Language Testing System (IELTS), of B1 level students of a public university in Ecuador. The methodology used for this study was descriptive, since it was based on describing the most relevant aspects of the problem under study, it also had a documentary approach so that relevant information was extracted from scientific journals, articles and books related to similar topics within the context of reading comprehension and the international IELTS test. As a result, it was found that many authors claim that if the student has prior knowledge, his reading comprehension quality would improve; However, McNeil (2011) refers to the opposite by considering that such knowledge does not act effectively at the time of reading, arguing that this knowledge corresponds to an independent variable in reading, implying that there is a possibility that the perception of B1 level students about their prior knowledge of the topic of a reading does not align with their performance in a reading section of an IELTS test.

Direct prediction for oceanic mesoscale eddy geospatial distribution through prior statistical deep learning

Mesoscale Eddy (ME) is a widely recognized and significant oceanic phenomenon characterized by extensive energy exchange. Accurate predictions of the future geospatial distribution of ME are crucial for maritime activities. Deep learning (DL) methods for ME prediction have consistently outperformed classical mathematical models and numerical modeling approaches. However, current DL methods based on two-dimensional gridded data suffer from error accumulation issues due to their indirect prediction approach, which involves first predicting the future environmental field and then identifying the ME patterns within it. Additionally, these models ignore the potential influence of prior knowledge related to ME on prediction outcomes. To address these issues, this paper proposes a direct prediction DL network called ES-ConvGRU, which incorporates historical prior statistics. First, we conduct an analysis of ME data from the Northwest Pacific region, extracting prior statistical knowledge such as interannual variations and seasonal characteristics. Second, mining patterns for the spatiotemporal features of both ME sequence and sea surface environmental field sequence are established. Furthermore, a nonlinear approach for mapping prior statistical knowledge and a multi-step autoregressive prediction model are designed, facilitating the integration of prior knowledge with multidimensional spatiotemporal features for ME prediction. Finally, the performance and effectiveness of ES-ConvGRU are evaluated. It achieves an average pixel accuracy of 93.21%, a mean intersection over union of 81.48%, and a frequency-weighted intersection over union of 87.55% for the 7-day distribution prediction. The results show that ES-ConvGRU exhibits notable advantages compared to the existing indirect and direct prediction methods.

Blame framing and prior knowledge influence moral judgments for people involved in the Tulsa Race Massacre among a combined Oklahoma and UK sample.

How an event is framed impacts how people judge the morality of those involved, but prior knowledge can influence information processing about an event, which also can impact moral judgments. The current study explored how blame framing and self-reported prior knowledge of a historical act of racial violence, labeled as Riot, Massacre, or Event, impacted individual's cumulative moral judgments regarding the groups involved in the Tulsa Race Massacre (Black Tulsans, the Tulsa Police, and White Tulsans). This study was collected in two cohorts including undergraduates attending the University of Oklahoma and individuals living in the United Kingdom. Participants were randomly assigned to a blame framing condition, read a factual summary of what happened in Tulsa in 1921, and then responded to various moral judgment items about each group. Individuals without prior knowledge had higher average Likert ratings (more blame) toward Black Tulsans and lower average Likert ratings (less blame) toward White Tulsans and the Tulsa Police compared to participants with prior knowledge. This finding was largest when what participants read was framed as a Massacre rather than a Riot or Event. We also found participants with prior knowledge significantly differed in how they made moral judgments across target groups; those with prior knowledge had lower average Likert ratings (less blame) for Black Tulsans and higher average Likert ratings (more blame) for White Tulsans on items pertaining to causal responsibility, intentionality, and punishment compared to participants without prior knowledge. Findings suggest that the effect of blame framing on moral judgments is dependent on prior knowledge. Implications for how people interpret both historical and new events involving harmful consequences are discussed.

Operation optimization of ventilated floor heating system in nearly-zero-energy building

As the share of renewable energy sources increases and building energy systems become more complex, optimizing system operation is necessary to fully improve economic, environmental, renewable penetration and thermal comfort performance. Most current studies focus on single-objective or multi-objective optimization of building systems, but the impact of different objectives and their combinations on operation is unclear. In this study, optimal schedules for a ventilated heating floor in a nearly-zero-energy building were determined using an improved particle swarm optimization algorithm coupled with prior knowledge-based search accelerating strategy. System performance was investigated under different combinations of optimization objectives, including reducing operational costs and carbon emissions, increasing wind power penetration, and improving thermal comfort. The results show that under the influence of prior knowledge, stochastic algorithm converges quickly with reasonable results even when the solution space is large. The performance of some single-objective optimization cases is unsatisfactory due to the exclusion of some control variables. In contrast, multi-objective optimization yields optimization problems better, which finds better dynamic operation status and leads to better system performance. Specifically, co-optimization of operational cost, wind power penetration and environmental cost together produces the most reasonable solution.

NewAbstractConcepts: A Database of 42 Normed Abstract Concepts and Exemplars.

Recently, researchers have expressed challenges in conducting word-learning experiments in adult populations due to limited availability of normed stimulus materials. This constraint often prompts the use of low-frequency or low-prevalence words, introducing the potential influence of prior knowledge or direct translation to familiar words. In response, we developed novel abstract concepts devoid of word referents, providing better control over prior knowledge. These new concepts describe situations encountered in various settings for which there is no existing word in English. The resulting database comprises 42 normed New Abstract Concepts, offering unique materials structured through scenarios, each containing similar and dissimilar exemplars. These materials underwent meticulous norming for relatability and similarity levels across a series of studies. The success of our approach was demonstrated in a word-learning experiment examining the effects of similarity and diversity. The database serves as a valuable resource for selecting stimuli in experiments exploring the learning of abstract semantic concepts, particularly investigating the role of similarity versus diversity in concept learning. The database is available on OSF (https://osf.io/svm2p/).

Multi-level knowledge-driven feature representation and triplet loss optimization network for image–text retrieval

Image–text retrieval plays a considerable role in associating vision and language. Existing mainstream approaches focus on fine-grained alignment while ignoring the influence of prior knowledge on model performance and the limitation of using a fixed margin in the triplet loss. In this paper, we propose a Multi-level Knowledge-driven feature representation and Triplet Loss Optimization Network (MKTLON) that exploits prior knowledge to enhance visual and textual feature representations and utilizes an adaptive margin of the triplet loss to optimize network training. Specifically, we first present an Enhanced feature Representation scheme based on the Self-Attention (ERSA) module, which incorporates the prior knowledge randomly initialized by uniform distribution into the matrices K and V in the self-attention mechanism. Subsequently, we adopt cascaded ERSA modules to encode images and texts to obtain multi-level visual and textual features with prior knowledge. Furthermore, we develop an adaptive margin optimization strategy that models the relevance scores of positive and negative samples as two independent Gaussian distributions, and obtain the optimized margin by minimizing the intersection of these two distributions. Extensive experiments on two benchmarks, Flickr30K (155,000 image–text pairs) and MSCOCO (616,435 image–text pairs), show the proposed MKTLON achieves 5.7% and 4.3% improvements on rSum, respectively, compared to the state-of-the-art method. The source code will be released at https://github.com/FlyCuteBird/MKTLON.

2 The Role of Causality in Understanding How Prior Event Knowledge Impacts New Learning

Objective:The influence of prior knowledge on new learning is well established. However, there has been less research dedicated to teasing apart the key components of prior knowledge’s structure that contribute to memory enhancement. In the current study, we focused on event structures, which include various relations, such as associative, causal, and temporal. Given that events possess attributes relevant to numerous cognitive memory processes, we were most interested in exploring how event structures that possess causal relations enhance new memory formation. Specifically, we examined whether events that exhibit causal associative relations provide an additional boost to new learning compared to event structures with non-causal associative relations.Participants and Methods:Forty-six undergraduate students took part in the study. Participants’ learning of the content of image pairs that exhibit everyday, real-world events were measured using a cued recall paradigm. The stimuli consisted of 60 image pairs that illustrated two events that were related causally and associatively (i.e., causal pairs); related only associatively (i.e., non-causal pairs); or not related at all (i.e., unrelated pairs). During an encoding phase, image pairs were presented one at a time, and after the presentation of each image pair, participants answered an encoding question that focused on the relationship between the two images. After the encoding phase and a short filler task, participants were shown a cue image (always the first picture from the pair) and were asked to provide a brief written description of the content of the second presented image from each pair. Also, as a manipulation check, we asked subjects to rate each image pair on causal direction and association strength after completion of the cued recall memory task.Results:We found that, relative to unrelated pairs, events that possess associative relations (i.e., both causal and non-causal items) benefit learning of new information. In addition, causal relations provided an additional boost to new learning. Specifically, cued recall performance is best for causal pairs, followed by non-causal pairs and unrelated pairs. Moreover, causal direction ratings significantly predict overall itemlevel accuracy above and beyond general associative relations that exist in events. We also examined recall accuracy for specific content information within each event (i.e., agent, action, object) and found that causal relations uniquely contribute to recall performance of objects and actions.Conclusions:Overall, the present study’s findings suggest that prior event knowledge structures possessing causal and non-causal associative relations support new learning, especially compared to image pairs with no relations. Of interest, causality provides an additional boost to new learning above and beyond general associative relations. By focusing on the role of causality in event structures, our findings informed our understanding of how prior knowledge supports new learning. Considering that the effect of prior knowledge on new episodic learning is especially evident in older adults, since they more readily rely on their schematic knowledge, a future direction would entail investigating how causal links influence new memory formation in older adults.

The Impact of Social Media Use on Entrepreneurial Opportunity: Evidence from Manipur, India

Objective: The present study aims to investigate the transformative influence of social media usage on entrepreneurial opportunity, recognition and exploitation in the unique context of Manipur, a northeastern state of India. This research aims to offer valuable insights into the dynamics of social media and entrepreneurship in a region which is characterized by its unique opportunities and challenges, ultimately contributing to a deeper understanding of the role of digital technology in fostering entrepreneurial growth.
 
 Theoretical Framework: This study is guided by seven hypotheses that lay the theoretical foundation, investigating the relationships among specific variables. The independent variable under study is the Social Media Usage (SMU) which is assumed to influence several aspects of entrepreneurial activity. Simultaneously, the study considers Prior Knowledge (PK), Entrepreneurial Alertness (EA) and Enhancing Social Networks (ESN) as intermediary variables, shaping the pathway from social media usage to entrepreneurial opportunity recognition and exploitation.
 
 Method: The authors surveyed 185 entrepreneurs in Manipur, India. Our study applied quantitative approaches to process data through PLS SEM.
 
 Results: The results of our study reveal that social media significantly enhances both entrepreneurial opportunity recognition and exploitation, as well as prior knowledge. However, Contrary to expectations, prior knowledge negatively influences entrepreneurial opportunity recognition and exploitation. Further, social media positively affects entrepreneurial alertness and has a significant impact on expanding social networks. However, the unexpected negative relationship between social networks and entrepreneurial opportunity recognition and exploitation was observed.
 
 Conclusion: In the context of Manipur, India, our study highlights social media’s pivotal role in boosting entrepreneurial opportunity recognition and exploitation, alongside positively affecting prior knowledge and alertness. However, the unexpected negative influence of prior knowledge and the surprising negative relationship between social networks and opportunities necessities further investigation.

Influence of prior knowledge on eye movements to scenes as revealed by hidden Markov models.

Human visual experience usually provides ample opportunity to accumulate knowledge about events unfolding in the environment. In typical scene perception experiments, however, participants view images that are unrelated to each other and, therefore, they cannot accumulate knowledge relevant to the upcoming visual input. Consequently, the influence of such knowledge on how this input is processed remains underexplored. Here, we investigated this influence in the context of gaze control. We used sequences of static film frames arranged in a way that allowed us to compare eye movements to identical frames between two groups: a group that accumulated prior knowledge relevant to the situations depicted in these frames and a group that did not. We used a machine learning approach based on hidden Markov models fitted to individual scanpaths to demonstrate that the gaze patterns from the two groups differed systematically and, thereby, showed that recently accumulated prior knowledge contributes to gaze control. Next, we leveraged the interpretability of hidden Markov models to characterize these differences. Additionally, we report two unexpected and interesting caveats of our approach. Overall, our results highlight the importance of recently acquired prior knowledge for oculomotor control and the potential of hidden Markov models as a tool for investigating it.