Mining Cognitive Skills Levels of Knowledge Units in Text Using Graph Tringluarity Mining

Nafa, Fatema; Khan, Javed; Othman, Salem; Babour, Amal

doi:10.1109/wiw.2016.013

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…When a knowledge unit is given the system finds out the hidden association in the knowledge unit using Tringluarity graph. This model can be used to give the new sequential ordering of knowledge units in a textbook [10]. Colin et.…”

Section: Literature Reviewmentioning

confidence: 99%

Random forest application on cognitive level classification of E-learning content

Thomas¹,

Chandra²

2020

IJECE

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The e-learning is the primary method of learning for most learners after the regular academics studies. The knowledge delivery through e-learning technologies increased exponentially over the years because of the advancement in internet and e-learning technologies. Knowledge delivery to some people would never have been possible without the e-learning technologies. Most of the working professional do focused studies for carrier advancement, promotion or to improve the domain knowledge. These learner can find many free e-learning web sites from the internet easily in the domain of interest. However it is quite difficult to find the best e-learning content suitable for their learning based on their domain knowledge level. User spent most of the time figuring out the right content from a plethora of available content and end up learning nothing. An intelligent framework using machine learning algorithms with Random Forest Classifier is proposed to address this issue, which classifies the e-learning content based on its difficulty levels and provide the learner the best content suitable based on the knowledge level .The frame work is trained with the data set collected from multiple popular e-learning web sites. The model is tested with real time e-learning web sites links and found that the e-contents in the web sites are recommended to the user based on its difficulty levels as beginner level, intermediate level and advanced level.

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Section: Literature Reviewmentioning

confidence: 99%

Random forest application on cognitive level classification of E-learning content

Thomas¹,

Chandra²

2020

IJECE

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“…Visualizing the concepts of one domain by using cognitive level mapped concepts graphs accomplished in [9]. The idea of automatically discovering and extracting the Bloom's Taxonomy from the text in one knowledge space is studied by Nafa, Khan and their colleagues [10]- [12].…”

Section: The Complexiity Of Accessising the Knowledgementioning

confidence: 99%

Inferring of Cognitive Skill Zones in Concept Space of Knowledge Assessment

Aboalela¹,

Khan²

2018

ijacsa

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Abstract-In these research zones of the knowledge, the assessed domain is identified. Explicitly, these zones are known as Verified Skills, Derived Skills and Potential Skills. In detail, the Verified Skills Zone is the set of tested concepts in the knowledge domain, while Derived Skills Zone is the set of the prerequisite concepts to the tested concepts based on the cognitive skills relation, whereas Potential Skills Zone is the set in the domain that have never been tested or prerequisite to the tested concepts but they are related to the tested concept based on the cognitive relation skills. Identifying cognitive relations between the concepts in one domain simplifies the structure of the assessment, which helps to find the knowledge state of the assessed individual in a short time and minimum number of questions. The existence of the concepts in the assessment domain helps us to estimate the set of the concepts that are known or not known or ready to be known or not ready to be known. In addition, it provides the output of the assessment in concept centric values in addition to the quantity values. The assessment result gives binary values of the assessed domain. "1" implies knowing the concept, whereas "0" implies not knowing the concept. The output is six sets of concepts: 1) Verified Known Skills; 2) Verified Not Known Skills; 3) Derived Known Skills; 4) Derived Not Known Skills; 5) Potential Known Skills; and 6) Potential Not Known Skills. The experiment is conducted to show the binary output of the assessed domain based on the participants' answers to the asked questions. The results also highlight the efficiency of the assessment.

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“…They introduced Topic Dependency Graph (TDG). Some research works combining the revised Bloom's Taxonomy with Knowledge Space such as that [6][7][8]. In these works, they automatically discovering and extracting the Bloom's Taxonomy from the text in one knowledge space.…”

Section: Introductionmentioning

confidence: 99%

Computation of Assessing the Knowledge in One Domain by using Cognitive Skills Levels

Aboalela¹,

Khan²

2018

IJCA

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Large number of questions are required to assess the knowing of the concepts in one domain. Novel methods are presented to map between the concepts in the domain and between the concepts and the questions of the assessment based on the cognitive skill prerequisite relation. The verbs of Bloom's Taxonomy are used to determine the cognitive skills levels. The cognitive skill levels refer to the levels such as if a learner has acquired the state at the level of understanding, or applying, or analyzing, or evaluating, or creating a concept. The state of achieving knowing or not knowing a certain concept state at certain skill level is called, concept state. Previously, three types of concept states proposed known as Verified Skills (VS), Derived Skills (DS) and Potential Skills (PS). VS and DS refers to the concept state that the concept is learned or not learned by the learner. PS refers to the concept state that the concept is ready to be learned or not ready to be learned. The experiment proved that using the cognitive relation between the concepts increases the amount of the estimated concepts, even though the number of tested concepts may be minimized and eliminated under the conditions laid down by the target cognitive skill levels. Also, the researchers compute the probability of knowing the concepts of two evaluated learners by using Bayes' Theorem.

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Mining Cognitive Skills Levels of Knowledge Units in Text Using Graph Tringluarity Mining

Cited by 4 publications

References 10 publications

Random forest application on cognitive level classification of E-learning content

Random forest application on cognitive level classification of E-learning content

Inferring of Cognitive Skill Zones in Concept Space of Knowledge Assessment

Computation of Assessing the Knowledge in One Domain by using Cognitive Skills Levels

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