Automatic estimation of exercises<b>'</b> difficulty levels in a tutoring system for teaching the conversion of natural language into first‐order logic

Perikos, Isidoros; Grivokostopoulou, Foteini; Kovas, Konstantinos; Hatzilygeroudis, Ioannis

doi:10.1111/exsy.12182

Cited by 11 publications

(25 citation statements)

References 18 publications

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“…They also conclude that combining both syntactic and semantic features allow more flexibility and generally gives better performance. (Perikos et al 2016 ) estimates the difficulty level of different exercises in natural language (NL) to the first order of logic (FOL). The difficulty is based on different parameters which is the FOL formula and it is also based on the semantics of exercise which is the Natural Language sentence.…”

Section: Literature Surveymentioning

confidence: 99%

Question paper generation through progressive model and difficulty calculation on the Promexa Mobile Application

et al. 2021

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Mobile learning has been increased in past years and has attracted the interests of academicians and educators in the past many years especially in higher education. The mobile-based online test is the buzzing in the current pandemic time. Institutions need to use online learning as a powerful tool for conducting exams and assess the students effectively. Integrating technology in education can be advantageous for universities and help engage better results for students. Therefore, it is important to understand each student their capacities and create a different test based on the required difficulty. Students should be graded based on their capabilities. The purpose of the research study is to develop the progressive model with the calibration of difficulty level according to the student capacity. To achieve the goal, a test of 20 python questions was conducted on 120 students with each question having difficulty given by 8 field experts. To verify the model, 5 categories were formed with different difficulty levels which in turn gave satisfactory results. To find a relation between the initial difficulty and the calculative difficulty based on the student response, a correlation test was conducted. After careful analysis of the question difficulty and student responses, it was observed that both are highly dependent on each other wherein the difficulty level of any question can be calculated using incorrect answers. The correlation coefficient obtained between them was 0.9833. Upon collecting the difficulty of the questions and student responses, respective grading could be done using the stated formula. Later on, the progressive model was simulated with five different cases (Best case, above-average case, below average case, the average case, worst case). The model outperformed in all the cases with appropriate difficulty levels. Online Tests have ushered a revolution in the assessment of students but yet they tend to be unpopular in India as the evaluation based on pen-paper approach is preferred. The main reasons for this are difficult to grade everyone at the same level, susceptible to cheating, and transition to open books. Using our study, universities can identify obstacles, and prepare an appropriate result-driven plan of action for implementing the mobile-based online test and make easy migration from paper-based test to online test.

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Section: Literature Surveymentioning

confidence: 99%

Question paper generation through progressive model and difficulty calculation on the Promexa Mobile Application

et al. 2021

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“…Early knowledge representation that uses modern notation is Classical logic, one of Formal Logical Systems. Some members of classical logic which are still being used up until now are Propositional Logic [22,23,24], First-Order Logic [25,26,27,28], and Second-Order Logics [29,30,31] among others. These logical systems are still being used and developed because their capability in knowledge reasoning.…”

Section: Knowledge Representationmentioning

confidence: 99%

Ontology Enrichment for Multi-Domain Knowledge and Expertise Representation

Achsan¹,

Suhartanto²,

Wibowo³

2017

DAAAM Proceedings

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Expertise is specialized skills and knowledge. It can be shared or disseminated if stated explicitly using expertise representation. Since people have multi area/domain expertise, we need multi-domain expertise representation. Someone's expertise can be recognized by his/her publication. In this article we proposed a new expertise representation by utilizing bibliography (publication metadata) and annotating ontology (ontology enrichment). The expertise representation is multi-domain that can be integrated semantically as easy as single-domain ontology.

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“…Conversion" (Perikos, Grivokostopoulou, Kovas, & Hatzilygeroudis, 2016), the authors present an intelligent tutoring systems (ITS) to assisting students in converting natural language sentences into firstorder logic. The assistance consists on an estimation of difficulty based on several factors involving the complexity of the formula, such as number, type and order of quantifiers, number of implications and connectives, among others.…”

Section: "Automatic Estimation Of Difficulty Level Of Exercises In a mentioning

confidence: 99%

“…By means of this framework, it is possible to develop multimodal interfaces that are a useful alternative to graphic user interfaces for mobile devices usually based in tapping through different menus. In “Automatic Estimation of Difficulty Level of Exercises in a Tutoring System for Teaching Natural Language into First Order Logic Conversion” (Perikos, Grivokostopoulou, Kovas, & Hatzilygeroudis, ), the authors present an intelligent tutoring systems (ITS) to assisting students in converting natural language sentences into first‐order logic. The assistance consists on an estimation of difficulty based on several factors involving the complexity of the formula, such as number, type and order of quantifiers, number of implications and connectives, among others.…”

mentioning

confidence: 99%