Measuring Domain Knowledge for Early Prediction of Student Performance: A Semantic Approach

Student performance is the most factor that can be beneficial for many parties, including students, parents, instructors, and administrators. Early prediction is needed to give the early monitor by the responsible person in charge of developing a better person for the nation. In this paper, the improvement of Bagged Tree to predict student performance based on four main classes, which are distinction, pass, fail, and withdrawn. The accuracy is used as an evaluation parameter for this prediction technique. The Bagged Tree with the addition of Bag, AdaBoost, RUSBoost learners helps to predict the student performance with the massive datasets. The use of the RUSBoost algorithm proved that it is very suitable for the imbalance datasets as the accuracy is 98.6% after implementing the feature selection and 99.1% without feature selection compared to other learner types even though the data is more than 30,000 datasets.

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A Trajectory Ontology Design Pattern for Semantic Trajectory Data Warehouses

Emerging Perspectives in Big Data Warehousing - Advances in Data Mining and Database Management ◽

10.4018/978-1-5225-5516-2.ch004 ◽

2019 ◽

pp. 83-104 ◽

Cited By ~ 1

Author(s):

Marwa Manaa ◽

Thouraya Sakouhi ◽

Jalel Akaichi

Keyword(s):

Case Studies ◽

Design Pattern ◽

Design Patterns ◽

Domain Knowledge ◽

Data Warehouses ◽

Trajectory Data ◽

Semantic Approach ◽

Mobility Data ◽

Ontology Design ◽

Ontology Design Pattern

Mobility data became an important paradigm for computing performed in various areas. Mobility data is considered as a core revealing the trace of mobile objects displacements. While each area presents a different optic of trajectory, they aim to support mobility data with domain knowledge. Semantic annotations may offer a common model for trajectories. Ontology design patterns seem to be promising solutions to define such trajectory related pattern. They appear more suitable for the annotation of multiperspective data than the only use of ontologies. The trajectory ontology design pattern will be used as a semantic layer for trajectory data warehouses for the sake of analyzing instantaneous behaviors conducted by mobile entities. In this chapter, the authors propose a semantic approach for the semantic modeling of trajectory and trajectory data warehouses based on a trajectory ontology design pattern. They validate the proposal through real case studies dealing with behavior analysis and animal tracking case studies.

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A Layered Architecture for a Fuzzy Semantic Approach for Satellite Image Analysis

International Journal of Knowledge and Systems Science ◽

10.4018/ijkss.2015040103 ◽

2015 ◽

Vol 6 (2) ◽

pp. 31-56 ◽

Cited By ~ 2

Author(s):

Cecilia Zanni-Merk ◽

Stella Marc-Zwecker ◽

Cédric Wemmert ◽

François de Bertrand de Beuvron

Keyword(s):

Domain Knowledge ◽

Contextual Information ◽

Satellite Image ◽

The Other ◽

Classification Methods ◽

Formal Representation ◽

Semantic Approach ◽

Layered Architecture ◽

Very High Spatial Resolution ◽

Knowledge Rules

The extended use of high and very high spatial resolution imagery inherently demands the adoption of classification methods capable of capturing the underlying semantic. Object-oriented classification methods are currently considered as the most appropriate alternative, due to the incorporation of contextual information and domain knowledge into the analysis. Integrating knowledge initially requires a detailed process of acquisition and later the achievement of a formal representation. Ontologies constitute a very suitable approach to address both knowledge formalization and exploitation. A novel semi-automatic fuzzy semantic approach focused on the extraction and classification of urban objects is hereby introduced. The use of a four-layered architecture allows the separation of concerns among knowledge, rules, experience and meta-knowledge. Knowledge represents the fundamental layer with which the other layers interact. Rules are meant to derive conclusions and make assertions based on knowledge. The experience layer supports the classification process in case of failure when attempting to identify an object, by applying specific expert rules to infer unusual membership. Finally, the meta-knowledge layer contains knowledge about the use of the other layers.

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A semantic approach based on domain knowledge for polarity shift detection using distant supervision

Progress in Artificial Intelligence ◽

10.1007/s13748-021-00267-x ◽

2021 ◽

Author(s):

Zahra Ayeste ◽

Samira Noferesti

Keyword(s):

Domain Knowledge ◽

Semantic Approach ◽

Distant Supervision ◽

Shift Detection

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Early Prediction of Student Performance in Blended Learning Courses Using Deep Neural Networks

2019 International Symposium on Educational Technology (ISET) ◽

10.1109/iset.2019.00018 ◽

2019 ◽

Author(s):

Rodolfo C. Raga ◽

Jennifer D. Raga

Keyword(s):

Neural Networks ◽

Blended Learning ◽

Student Performance ◽

Deep Neural Networks ◽

Early Prediction

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Massive LMS log data analysis for the early prediction of course-agnostic student performance

Computers & Education ◽

10.1016/j.compedu.2020.104108 ◽

2021 ◽

Vol 163 ◽

pp. 104108

Author(s):

Moises Riestra-González ◽

Maria del Puerto Paule-Ruíz ◽

Francisco Ortin

Keyword(s):

Data Analysis ◽

Student Performance ◽

Early Prediction ◽

Log Data ◽

Log Data Analysis

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A Semantic Approach for Quality Assurance and Assessment of Volunteered Geographic Information

Information ◽

10.3390/info12120492 ◽

2021 ◽

Vol 12 (12) ◽

pp. 492

Author(s):

Gloria Bordogna

Keyword(s):

Quality Assurance ◽

Domain Knowledge ◽

Epistemic Uncertainty ◽

Volunteered Geographic Information ◽

Geographic Information ◽

Semantic Approach ◽

Fuzzy Ontology ◽

Volunteer Geographic Information

The paper analyses the characteristics of Volunteer Geographic Information (VGI) and the need to assure and assess its quality for a possible use and re-use. Ontologies and soft ontologies are presented as means to support quality assurance and assessment of VGI by highlighting their limitations. A proposal of a possibilistic approach using fuzzy ontology is finally illustrated that allows to model both imprecision and vagueness of domain knowledge and epistemic uncertainty affecting observations. A case study example is illustrated.

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SEMANTIC APPROACH TO SMART CONTRACT VERIFICATION

Facta Universitatis Series Automatic Control and Robotics ◽

10.22190/fuacr2001021p ◽

2020 ◽

Vol 19 (1) ◽

pp. 021

Author(s):

Nenad Petrović ◽

Milorad Tošić

Keyword(s):

Domain Knowledge ◽

Expert Knowledge ◽

Semantic Representation ◽

Formal Semantics ◽

Source Code ◽

Limiting Factors ◽

Smart Contracts ◽

Semantic Approach ◽

Blockchain Technology ◽

Smart Contract

Vulnerabilities of smart contract are certainly one of the limiting factors for wider adoption of blockchain technology. Smart contracts written in Solidity language are considered due to common adoption of the Ethereum blockchain platform. Despite its popularity, the semantics of the language is not completely documented and relies on implicit mechanisms not publicly available and as such vulnerable to possible attacks. In addition, creating formal semantics for the higher-level language provides support to veriﬁcation mechanisms. In this paper, a novel approach to smart contact verification is presented that uses ontologies in order to leverage semantic annotations of the smart contract source code combined with semantic representation of domain-specific aspects. The following aspects of smart contracts, apart from source code are taken into consideration for verification: business logic, domain knowledge, run-time state changes and expert knowledge about vulnerabilities. Main advantages of the proposed verification approach are platform independence and extendability.

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