결정적 학습 경로를 위한 지식 구조 분석 시스템

The knowledge space theory can better understand the knowledge concept and its internal connection by analyzing and understanding the connection and combination of knowledge concepts. A personalized learning resource recommendation framework based on knowledge space theory is designed to help students better understand and master subject knowledge in this paper. The framework mainly includes the following three steps: firstly, analyze the students' learning behavior, and establish the student knowledge space model; secondly, establish the subject knowledge space model by analyzing the structure and relationship of subject knowledge; finally, according to the student's The knowledge space model and subject knowledge space model design personalized learning resource recommendation algorithms to provide students with the most suitable learning resources. Experiments are conducted to verify the validity and feasibility of the framework, as well as its impact on student learning outcomes.

A crucial problem in knowledge space theory, a modern psy chological test theory, is the derivation of a realistic knowledge structure representing the organization of knowledge in an information domain and examinee population under reference. Often, one is left with the problem of selecting among candidate competing knowledge structures. This article proposes a measure for the selection among competing knowledge structures. It is derived within an operational framework (prediction paradigm), and is partly based on the unitary method of proportional reduction in predictive error as advocated by the authors Guttman, Goodman, and Kruskal. In particular, this measure is designed to trade off the (descriptive) fit and size of a knowledge structure, which is of high interest in knowledge space theory. The proposed approach is compared with the Correlational Agreement Coef ficient, which has been recently discussed for the selection among competing surmise relations. Their performances as selection measures are compared in a simulation study using the fundamental basic local independence model in knowledge space theory

Learning, forgetting, and the correlation of knowledge in knowledge space theory

Knowledge Spaces

BLIM’s identifiability and parameter invariance under backward and forward transformations

In this paper, we first construct the discourse knowledge structure of education in different disciplines so that the knowledge points in a certain cross-cutting area between different disciplines are finite sets called knowledge domains, and we use the network of the discourse field system to characterize the logical relations in the knowledge space. Secondly, by defining the elements of hypertext as a triadic structure, a specific chain of dependencies is constructed by combining the hypertext structure with the knowledge space. Finally, we conducted a comparative analysis of the knowledge structure of educational discourse in various disciplines. The results show that among 45 knowledge states, 15 attributes corresponding to the whole column have all 0 information values and the knowledge structure model based on the knowledge space can master the corresponding effective skills of students in different disciplines, which indicates that it realizes this learning process of knowledge learning from point to point, from concrete to abstract, and from part to the whole under different disciplines. This paper examines the use of discourse in various knowledge structures, which may provide insights into the teaching of English academic writing in China.

In the early eighties of the last century three theories were independently developed in the fields of information science, mathematical psychology and cognitive psychology. In 1982 Rudolph Wille conceptualized a principled way of deriving a concept hierarchy from a collection of objects and their properties: the Formal Concept Analysis. Such representation draws upon the order theory developed in 1937 by Birkhoff about the one-to-one correspondence between distributive lattices and partial orders. In 1985 Jean Claude Falmagne and Jean-Paul Doignon, starting from Birkhoff’s theorem, developed a theory to represent the prerequisite relations among sets of problems in the field of knowledge assessment: The Knowledge Space Theory. In 1983 Kikumi Tatsuoka introduced the concept of a Q-Matrix defining the basis for the development of a skill based way for cognitive diagnosis: The Cognitive Diagnostic Models. It is an interesting issue how, so far, only few attempts to highlight similarities among these theories have been carried out. This research is aimed at pinpointing some of the main deterministic and probabilistic concepts that, even if named differently, are shared by the three theories.

The present work explores the connections between cognitive diagnostic models (CDM) and knowledge space theory (KST) and shows that these two quite distinct approaches overlap. It is proved that in fact the Multiple Strategy DINA (Deterministic Input Noisy AND-gate) model and the CBLIM, a competence-based extension of the basic local independence model (BLIM), are equivalent. To demonstrate the benefits that arise from integrating the two theoretical perspectives, it is shown that a fairly complete picture on the identifiability of these models emerges by combining results from both camps. The impact of the results is illustrated by an empirical example, and topics for further research are pointed out.

Knowledge space theory is part of psychometrics and provides a theoretical framework for the modeling, assessment, and training of knowledge. It utilizes the idea that some pieces of knowledge may imply others, and is based on order and set theory. We introduce the R package <b>DAKS</b> for performing basic and advanced operations in knowledge space theory. This package implements three inductive item tree analysis algorithms for deriving quasi orders from binary data, the original, corrected, and minimized corrected algorithms, in sample as well as population quantities. It provides functions for computing population and estimated asymptotic variances of and one and two sample Z tests for the diff fit measures, and for switching between test item and knowledge state representations. Other features are a function for computing response pattern and knowledge state frequencies, a data (based on a finite mixture latent variable model) and quasi order simulation tool, and a Hasse diagram drawing device. We describe the functions of the package and demonstrate their usage by real and simulated data examples.

In this paper, an automatic lesson generation system is presented which is suitable in a learning-by-mimicking scenario where the learning objects can be represented as multiattribute time series data. The dance is used as an example in this paper to illustrate the idea. Given a dance motion sequence as the input, the proposed lesson generation system automatically generates the lesson plan for students. It first extracts patterns from the input dance sequence to form the learning objects. The prerequisite structure is then built by considering the relations between the learning objects. Afterward the knowledge structure is constructed from the prerequisite structure based on the knowledge space theory. Finally, the learning path is derived according to an easy-to-complex manner while respecting the prerequisite relations. A user study that involved 40 students was conducted to evaluate the proposed work. The average learning time required for the treatment group (learning with the proposed system) was found to be lower than that of the control group (learning by free browsing) thus demonstrating the learning efficiency of the proposed system. The feedback from the questionnaires indicated that a majority of the subjects showed positive response toward the usefulness and rationality of our proposed system.

We report a comparative study using knowledge space theory (KST) to assess the impact of a hands-on laboratory exercise that used molecular model kits to emphasize the connections between a plane of symmetry, chirality, and isomerism in an introductory organic chemistry course. The experimental design compared three groups of students-two that carried out the laboratory exercise (treatment groups) and one that did not (control group). Our assessments revealed that all three groups made significant improvements in their understanding of chirality and isomerism, but that the two treatment groups showed more improvements than the control group. The KST analysis showed that the acquisition of the skills for finding a plane of symmetry in a molecule came late in the students' critical learning pathways, which indicated that this was difficult for many students. Students were unfamiliar with this type of reasoning, and our studies revealed that the laboratory exercise was useful in teaching the method to introductory organic chemistry students.

Automata for knowledge assessment based on the structure of observed learning outcome taxonomy

In formative assessments, one wants to provide a useful feedback to the examinee at the end of the test. In order to reduce the number of questions asked in an assessment, adaptive testing models have been developed for cognitive diagnosis, such as the ones encountered in knowledge space theory. However, when the number of skills assessed is very huge, such methods cannot scale. In this paper, we present a new method to provide adaptive tests and useful feedback to the examinee, even with large databases of skills. It will be used in Pix, a platform for certification of digital competencies for every French citizen.

The authors investigate the relationship between student learning choices and learning styles in the use of an innovative learning environment for statistics. For teaching introductory statistics to first-year students in economics and business, Maastricht University uses a blended learning environment. It allows students to individualize learning by attuning the available learning tools to their personal preferences. The blended learning environment consists of tutorials focused on problem-based learning, lectures, independent learning and an electronic learning environment (ALEKS) based on knowledge space theory. The paper examines the intensity of use of ALEKS and investigates the relationship between this and student learning dispositions in a correlational study. Data from around 4,650 students are used. The findings suggest that less academically-inclined students, such as those with ‘surface’ approaches to learning, profit most from the availability of e-learning tools.

Given a collection Q of problems, in knowledge space theory Doignon & Falmagne, (International Journal of Man-Machine Studies 23:175-196, 1985) the knowledge state of a student is the collection K ⊆ Q of all problems that this student is capable of solving. A knowledge structure is a pair (Q, ), where is a collection of knowledge states that contains at least the empty set and Q. A probabilistic knowledge structure (PKS) is a knowledge structure (Q, , π), where π is a probability distribution on the knowledge states. The PKS that has received the most attention is the basic local independence model BLIM; Falmagne & Doignon, (British Journal of Mathematical and Statistical Psychology 41:1-23, 1988a, Journal of Mathematical Psychology 32:232-258, 1988b). To the best of our knowledge, systematic investigations in the literature concerning the identifiability of the BLIM are totally missing. Based on the theoretical work of Bamber and van Santen (Journal of Mathematical Psychology 29:443-473, 1985), the present article is aimed to present a method and a corresponding computerized procedure for assessing the local identifiability of the BLIM, which is applicable to any finite knowledge structure of moderate size.