Cognitive Knowledge Structures Research Articles

Modeling Not-Reached Items in Cognitive Diagnostic Assessments.

In cognitive diagnostic assessments with time limits, not-reached items (i.e., continuous nonresponses at the end of tests) frequently occur because examinees drop out of the test due to insufficient time. Oftentimes, the not-reached items are related to examinees’ specific cognitive attributes or knowledge structures. Thus, the underlying missing data mechanism of not-reached items is non-ignorable. In this study, a missing data model for not-reached items in cognitive diagnosis assessments was proposed. A sequential model with linear restrictions on item parameters for missing indicators was adopted; meanwhile, the deterministic inputs, noisy “and” gate model was used to model the responses. The higher-order structure was used to capture the correlation between higher-order ability parameters and dropping-out propensity parameters. A Bayesian Markov chain Monte Carlo method was used to estimate the model parameters. The simulation results showed that the proposed model improved diagnostic feedback results and produced accurate item parameters when the missing data mechanism was non-ignorable. The applicability of our model was demonstrated using a dataset from the Program for International Student Assessment 2018 computer-based mathematics cognitive test.

THE DYNAMIC IMAGE OF CHINA PRESENTED IN GERMAN MAINSTREAM MEDIA REPORTS --THE CHINA-RELATED REPORTS OF GERMAN MAINSTREAM MEDIA FROM JANUARY 2020 TO JANUARY 2021 WERE USED AS EXAMPLES

The impact of the COVID-19 pandemic, which began to spread globally in early 2020, has attracted a great deal of attention from global society on the world's politics, economy, public security and global development. The mass media played an important role in promoting it, making international relations and the construction of national images during COVID-19 pandemic an important and urgent issue of national interest in the international political arena. During this period, China also became the ‘centre of public opinion’. As the most inuential country in the European Union, Germany's foreign policy and the China policy under its presidency has attracted a great deal of global attention. While Sino-German cooperation remained at a ‘high level’ during the pandemic, Germany also sought to position itself between the great powers of China and the US. Therefore, the image of China presented by the German mainstream media in their coverage of China during the COVID-19 pandemic, their positions, the constraints behind them, and their development trends are worthy of attention and study by academics. This study attempts to research the generation of Chinese images in the German mainstream media, including both an analysis of "Germany" and a reection on "China", and to investigate what cognitive models and knowledge structures the German mainstream media use in their discourse construction, how they use their discursive power, and what kind of Chinese national image they construct

Ambiguous, misinterpreted, but essential: conceptualization of the “digital mindset”

The way people how act and think is mentioned frequently as one of the most essential factors to consider regarding successful digitalization. However, although we frequently talk about the ‘digital mindset’, the literature lacks a distinct, holistic and detailed definition of what this way of thinking is actually representing. Through an analysis and synthesis of the fields of psychology and digitalization literature, this conceptual paper pursues the goal to address the question of how a digital mindset can be conceptualized from a holistic perspective. As a re-sult, three main findings contribute to the research of the human side of digitalization and pave the way for future research avenues. First, a general definition of digital mindset is formed as thinking patterns, epitomized through cognitive processes, filters, and core convic-tions of humans constituted of cognitive mechanisms and knowledge structures, that affect and foster digital innovation and transformation to accomplish digitalization. Secondly, 19 concrete thinking patterns are derived that typify a digital mindset and can be differentiated between newly developed thinking patterns and remained or modified thinking patterns. Thirdly, the conceptualization reveals that the development of a (digital) mindset is based on knowledge as a prime component.

Extending the general aggression model: contributions of DSM-5 maladaptive personality facets and schema modes

ABSTRACTThis study sought to determine whether the addition of DSM-5 personality facets and schema modes improved the prediction of aggression history over and above General Aggression Model (GAM) cognitive knowledge structures (aggressive script rehearsal and normative beliefs supportive of aggression) and anger. Participants were 208 incarcerated adult male prisoners who completed a battery of self-report psychological tests assessing the aforementioned constructs. Results indicated that aggression-related cognitive knowledge structures, anger, and various personality facets and schema modes were significantly associated with aggression. However, only anger, the personality facet Risk Taking, and the Enraged Child schema mode uniquely accounted for variance in multivariable modelling. This suggests that consideration of maladaptive personality facets, specifically risk taking, and schema modes characterised by intense anger and destructive coping behaviour, alongside established aggression-related cognitive and affective constructs, can (a) extend theoretical knowledge of the specific contents and processes involved in increasing aggression propensity, and (b) improve GAM’s application in clinical settings, particularly in regards to assessment and treatment of violent offenders.

MOOCRC: A Highly Accurate Resource Recommendation Model for Use in MOOC Environments

With the rapid development of MOOC platforms, the online learning resources are increasing. Because learners differ in terms of cognitive ability and knowledge structure, they cannot rapidly identify learning resources in which they are interested. Traditional collaborative filtering recommendation technologies perform poorly given sparse data and cold starts. Furthermore, the redundant recommended content and the high-dimensional and nonlinear data on online learning users cannot be effectively handled, leading to inefficient resource recommendations. To enhance learner efficiency and enthusiasm, this paper presents a highly accurate resource recommendation model (MOOCRC) based on deep belief networks (DBNs) in MOOC environments. This method deeply mines learner features and course content attribute features and incorporates learner behavior features to build user-course feature vectors as inputs to the deep model. Learner ratings of courses are processed as supervised labels with supervised learning. The MOOCRC model is trained by unsupervised pretraining and supervised feedback fine tuning; moreover, the model is obtained by adjusting the model parameters repeatedly. To verify the effectiveness of the MOOCRC, an experimental analysis is conducted using learner elective data obtained from the starC MOOC platform of Central China Normal University. Real course enrollment data are used to verify the classification accuracy of the MOOCRC. The experimental results show that the MOOCRC has greater recommendation accuracy and converges more quickly than traditional recommendation methods.

Influence of using challenging tasks in biology classrooms on students’ cognitive knowledge structure: an empirical video study

ABSTRACTEmpirical analysis of secondary biology classrooms revealed that, on average, 68% of teaching time in Germany revolved around processing tasks. Quality of instruction can thus be assessed by analyzing the quality of tasks used in classroom discourse. This quasi-experimental study analyzed how teachers used tasks in 38 videotaped biology lessons pertaining to the topic ‘blood and circulatory system’. Two fundamental characteristics used to analyze tasks include: (1) required cognitive level of processing (e.g. low level information processing: repetiition, summary, define, classify and high level information processing: interpret-analyze data, formulate hypothesis, etc.) and (2) complexity of task content (e.g. if tasks require use of factual, linking or concept level content). Additionally, students’ cognitive knowledge structure about the topic ‘blood and circulatory system’ was measured using student-drawn concept maps (N = 970 students). Finally, linear multilevel models were created with high-level cognitive processing tasks and higher content complexity tasks as class-level predictors and students’ prior knowledge, students’ interest in biology, and students’ interest in biology activities as control covariates. Results showed a positive influence of high-level cognitive processing tasks (β = 0.07; p < .01) on students’ cognitive knowledge structure. However, there was no observed effect of higher content complexity tasks on students’ cognitive knowledge structure. Presented findings encourage the use of high-level cognitive processing tasks in biology instruction.

Tracing Attitude Expressions: An Eye‐Tracking Study

AbstractAttitudes represent object evaluations, comprising complex underlying cognitive and affective knowledge structures. When people are asked to judge an object, they can use their primary response (i.e., the immediate object‐evaluation linkage) or underlying affective and cognitive knowledge structures. In many situations, a primary response satisfices, but if not, more elaboration is required. Both processes are fundamentally different but may lead to the same attitude. For monitoring underlying processes during attitude expression, we developed an innovative eye‐tracking procedure using eye‐gaze on response scale options. This procedure was applied in three studies to identify the extent to which elaboration differs for attitude objects with weak or strong, univalent or mixed object evaluations (i.e., univalent, neutral and ambivalent). In Study 1, the overall judgment preceded processing of more specific affective and cognitive linkage evaluations. In Studies 2 and 3, the order was reversed, and affective and cognitive bases were assessed prior to overall attitude outcomes. For attitude objects with strong univalent or strong mixed object evaluations, we found similar outcomes on underlying processes. For weak object evaluations, cognition was found to be more predictive and easily accessible if an overall judgment was required first; affect for these objects was more predictive if people had to elaborate on affect and cognition first. We concluded that both affective and cognitive attitudes may require substantial elaboration, albeit in different situations. Copyright © 2015 John Wiley &amp; Sons, Ltd.

Leader Knowledge and Customer-Focused Knowledge Management: Innovation and Performance

The strategic leadership literature on innovation has neither addressed leader knowledge structures nor their reflections in organizational processes such as knowledge management vis-a-vis crucial external entities such as customers. We contribute to this literature by examining the cognitive knowledge structures (in the form of cause-effect beliefs) of leaders and the processes of knowledge management vis-a-vis customers in entrepreneurial settings. Specifically, we examine the impact of leader knowledge and processes of customer-focused knowledge management respectively in the innovation process leading to performance. Using survey data from 220 senior managers in output functions from India, we find support for the proposed theoretical model. We test and find support for the moderating impact of intra-firm causal ambiguity. We discuss these results in the entrepreneurial contexts provided by emerging markets and potential generalizability to other settings. We discuss these findings in the context of t...

Theorizing change in artificial intelligence: inductivising philosophy from economic cognition processes

Economic value additions to knowledge and demand provide practical, embedded and extensible meaning to philosophizing cognitive systems. Evaluation of a cognitive system is an empirical matter. Thinking of science in terms of distributed cognition (interactionism) enlarges the domain of cognition. Anything that actually contributes to the specific quality of output of a cognitive system is part of the system in time and/or space. Cognitive science studies behaviour and knowledge structures of experts and categorized structures based on underlying structures. Knowledge representation through understanding of `epistemic cultures' is an evolutionary stage. But cognition goes beyond knowledge representation. Notwithstanding the importance of epistemology of phenomena, the practicability cum philosophical aspects of machine learning needs to be seen in dynamic behaviour in socio-economic-technical value additions if human machine interaction processes that are context specific are incorporated into strong artificial intelligent systems. Cognitive Science is also studied from both computational and biological angles. Evolution of interactive forms of reasoning through understanding of meta-language of computations or biological learning processes is possible. But the limitation of historical cultures predefines the role of interactive processes in user-networks beyond technology networks. Despite this limitation, inclusive development notions of a heterogeneous national society such as India or Europe can be tested and incorporated.

Mental Model Updating and Team Adaptation

In this article, we build on theories of team adaptation by exploring the role of team members’ cognitive knowledge structures in team adaptation to a changing task context. We introduce the notion of mental model updating as the extent to which team members update their mental models in reaction to a change in the task situation. In a laboratory study we investigate the relations between initial mental model similarity and accuracy, team mental model updating, the development of novel interaction patterns, and postchange team performance. The results indicate that mental model updating is positively related to postchange team performance. Also, team adaptation patterns accounted for the effect of mental model updating on postchange team performance. We did not find evidence for a positive relation between initial mental model similarity and accuracy and mental model updating.

Strategies of Theoretical Physics Instruction Reform

Theoretical physics is the main constitute part of physics science. The instruction of theoretical physics courses plays an important role in the research of basic science and training physics talents. Most of students considered that the knowledge of theoretical physics is very abstract and causing many difficulties in study. But students have initiatives to learn theoretical physics well. Teachers can implement some teaching reform strategies to improve the quality of theoretical physics instruction. The strategies are as follow: stimulate students’ interest in learning, perfect students' cognitive structure and knowledge structure of theoretical physics; optimize the system of theoretical physics curriculum, enrich the instruction contents; apply heuristic instruction in theoretical physics teaching; establish virtual theoretical physics experiments and improve assessment and appraisal methods, promote students' all-round development.

Cognitive Knowledge Structure and Information Seeking Framework to Reduce Cognitive Burden

Cognitive Knowledge Structure and Information Seeking Framework to Reduce Cognitive Burden

LIM-G: Learner-initiating instruction model based on cognitive knowledge for geometry word problem comprehension

Computer-assisted instruction systems have been broadly applied to help students solve math word problem. The majority of such systems, which are based on an instructor-initiating instruction strategy, provide pre-designed problems for the learners. When learners are asked to solve a word problem, the system will instruct the learners what to do. However, systems employing an instructor-initiating instruction strategy offer little help to advanced learners or to learners encountering problems that are not in the pre-designed database. Therefore, in this study, a learner-initiating instruction model (LIM-G) is proposed to help learners’ comprehension of geometry word problems. Geometry word problems are math word problems involving geometric concepts. Many researches indicate that learners encounter difficulties while comprehending math word problems. In this model, a learner can seek help with any geometry word problem he is interested in. Based on a learner-initiating instruction strategy, LIM-G first comprehends the problem and then gives the learner the telegraphic and diagrammatic representations of the problem, which are more intuitive to understand. For LIM-G, the comprehension mechanism plays a critical role in solving word problems. For this study, a system is built based on LIM-G. In this system, the cognitive knowledge needed for comprehending geometry word problem is constructed with an ontology-based tool called InfoMap. Using cognitive knowledge and frame-template structures, the system can extract the relevant concepts in geometry word problems for comprehension.

A Cognitive-Ecological Approach to Serving Students with Emotional and Behavioral Disorders: Application to Aggressive Behavior

In this article we present a cognitive-ecological model for understanding and preventing emotional and behavioral difficulties and propose directions for school-based intervention programs, particularly with aggressive children. In the cognitive-ecological framework, intervention efforts should target certain cognitive skills (e.g., skills that encourage attention to multiple cues in a setting) and knowledge structures (e.g., normative beliefs about appropriate responses to conflict) across multiple contexts that change over time (e.g., classroom, peer, school, family). We also emphasize the importance of coordination among contextual influences so that children learn consistent, cross-context standards that encourage prosocial and socially competent behavior. Practitioners working with students who exhibit emotional and behavioral difficulties should strive to integrate efforts at modifying cognition as well as context in the service of promoting behavioral change that maintains over time and across situations.

Modelling the Groove: Conceptual Structure and Popular Music

A musical groove is most typically created by a small group of musicians working together, each contributing parts to the whole. Characterizing the knowledge behind such interactive ventures has proved to be challenging. This article attempts to characterize the knowledge basic to grooves first by concentrating on ‘the groove’ as it is practised in soul, rhythm and blues, jazz fusion and various other popular genres, and second by focusing on cognitive knowledge structures called conceptual models. It is argued that musicians rely on such structures to produce grooves, and that listeners make use of similar structures to understand them. Grooves from the music of Eric Clapton, Miles Davis and James Brown are discussed, and conceptual models for each are developed.

On relevance again: From philosophy of language across ‘pragmatics and power’ to global relevance

This paper, largely motivated by Harris (1995), revisits the issue of the Cooperative Principle and, in particular, relevance. I would, firstly, like to note that even before the emergence of empirical pragmatics and critical discourse analysis there had been raised, albeit within a philosophical perspective, some questions relating to language and power and the universality across discourses of the CP. Secondly, I would like to draw attention to the pervasive nature of the maxim of relevance, which, however, needs to be seen at a global level as a forceful social parameter governing linguistic communication or ‘transaction’ and as contingent on typifications of social situations described in terms of cognitive knowledge structures. It is claimed that, just as language is firmly placed within structured social domains or events, so too linguistic behaviour within them is structured and largely predictable as enjoined by the structure of those events and domains, represented in our conceptual world. The paper argues for the postulation of a socially determined supermaxim of Global Relevance, embedded within the actional structure of representations of events. As a consequence, a more complete account of what has been called the Cooperative Principle has to lie at the intersection of a cognitive theory and a social theory of language use.

Assessing Knowledge Structures: Relations With Experience and Posttraining Performance

This research focuses on the construct validity of two methodologies for assessing cognitive organization or knowledge structures. Validity evidence is evaluated by considering the complex interrelations among experience, knowledge structures, and training outcomes. These relations are explored in the context of a laboratory-based, computer-related training initiative. In this article, results are presented that suggest that certain types of prior experience and some indicators of knowledge structures have significant relations with posttraining performance. However, the validity evi- dence gathered in this study does not support the notion that the two knowledge struc- ture assessment methods used are capturing equivalent aspects of knowledge. Moreover, context effects are indicated such that the relations among knowledge structures, experience, and training outcomes vary as a function of the set of concepts used in structural assessment. General implications for research on training, knowl- edge acquisition, and performance are considered.

An investigation of patterns of discontinuous learning: implications for ESL measurement

Research from cognitive psychology, L2 acquisition and psychometry sug gests that competence/proficiency is multidimensional, and that proficiency does not always develop at the same rate in all its domains. In particular, L2 acquisition research indicates that competence does not always develop in a linear, monotonic pattern at an equal rate in all its component parts owing to restructuring and reorganization. Rehearsal, automatic and controlled pro cessing, creative simplification, task constraints and attention to form have been adduced to explain restructuring and reorganization. Classical test theory and item response theory models are not consonant with findings from cognitive psychology and L2 acquisition, because these models pinpoint individuals on a single continuum of proficiency. The study reported here tested the experimental hypothesis that there would not be a monotonic relationship between increasing competence in different linguistic domains and growth in L2 proficiency. There was general support for the hypothesis. The article concludes with a brief introduction to some assessment models which can accommodate discontinuous patterns of growth. The models examined can be used to describe and model a student's qualitative cognitive state and knowledge structures.

Measurement Model for Linking Individual Learning to Processes and Knowledge: Application to Mathematical Reasoning

Contemporary instructional theories increasingly emphasize the importance of linking an individual's learning to changes in cognitive processes and knowledge structures. In this article, an extension of the multidimensional Rasch model for learning and change (MRMLC) is presented so as to permit theories of processes and knowledge structures to be incorporated into the item response model. Like the MRMLC, this extension (MRMLC+) resolves some basic problems in measuring individual change and permits adaptive testing so that precise estimates of learning may be obtained. Additionally, MRMLC+ permits individual learning to be linked to substantive changes in processing and knowledge. An application to a study on the impact of short‐term instruction on mathematical problem solving shows the potential of MRMLC + for interpretations. In this study, a theoretically plausible model of knowledge structures (Mayer, Larkin, &amp; Kadane, 1984) provides the basis of individual learning interpretations.

Gender differences in adults’ knowledge about fat and cholesterol

Cognitive learning theories suggest that an individual's prior knowledge is a major factor for determining what can be learned. The objectives of this study were to compare the organization of knowledge about fat and cholesterol in adult, middle-class men and women and changes in that knowledge after reading an educational bulletin. Forty men and 48 women participated in two semistructured interviews to assess their knowledge; half received a US Department of Agriculture bulletin on fat and cholesterol at the end of the first interview. Concept maps (two-dimensional representations of an individual's cognitive knowledge structure) were made from the interview transcripts to assess knowledge organization. Both genders had limited knowledge, scoring less than 25% of the maximum possible score at interview 1. Overall, known and not known concepts were similar in men and women; however, women had slightly more integrated knowledge and more misconceptions than men. Both groups receiving the bulletin made significant gains in knowledge at interview 2, but knowledge gains were twice as great in men as in women. Our findings indicate that practitioners need not create separate materials about fat and cholesterol for middle-class men and women.