Towards an Automatic Q&amp;A Generation for Online Courses - A Pipeline Based Approach

Rüdian, Sylvio; Pinkwart, Niels

doi:10.1007/978-3-030-23207-8_44

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…The advantage of the imitational generator is also that it does not need primary content databases, which in conventional sources (Dorodchi, Al-Hossami, Benedict, & Demeter, 2019;Rüdian & Pinkwart, 2019) usually are a source for modifying the content. In addition, the use of optimization calculation methods can significantly expand the scope of tasks.…”

Section: Discussionmentioning

confidence: 99%

“…Although various personalization methodologies in an educational context already exist, personalization by context is always limited by the existing educational material; their creation is a laborious task. A conveyor is presented for generating questions and correct answers based on training texts (Rüdian & Pinkwart, 2019), limited to actual questions for given sentences. NLP technologies are combined with an effective methodology and adjusted to create pairs of questions and answers.…”

Section: Introductionmentioning

confidence: 99%

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Triangle Generator for Online Mathematical E-learning

Yavich¹,

Malev²,

Rotkin³

2020

HES

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What constitutes learning in the 21st century is the capacity for e-learning. Integral components of e-education are training content management systems. Existing content generators more transform content than create original content. Creating a methodology and technology for generating original content is important and relevant. In order to form adequate methods for generating content, primarily for mathematical and related disciplines, the problem of generating a triangle, together with its many attributes, is considered as the simplest object of elementary mathematics. We create a simulation model that describes the properties of the object and apply modified methods of optimization and corresponding algorithms to it. The current algorithmic layout demonstrates the performance of the developed system. It generates triangles in any configurations matching the given parameters and demonstrates their properties.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Triangle Generator for Online Mathematical E-learning

Yavich¹,

Malev²,

Rotkin³

2020

HES

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“…Most of the automated question generation systems are associated with texts that often require the use of natural language analysis techniques [13]- [15]. Our approach focuses on the evaluation of procedural knowledge [16], specifically Comparative table of SIETTE features with other tools that address similar educational tasks published along last decade: LISA [2], SESHAT [3], PROLETOOL [4], PAMOJA [5] and COMVIS [6] This…”

Section: Introductionmentioning

confidence: 99%

Teaching Compilers: Automatic Question Generation and Intelligent Assessment of Grammars' Parsing

Muñoz,

Blanco,

Campo-Ávila

et al. 2024

IEEE Trans. Learning Technol.

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Automatic question generation and the assessment of procedural knowledge is still a challenging research topic. This article focuses on case of it, the techniques of parsing grammars for compiler construction. There are two well-known techniques for parsing: top-down parsing with LL(1) and bottomup with LR(1). Learning these techniques and learning to design grammars that can be parsed with these techniques requires practice. This article describes an application that covers all the tasks needed to automatise the learning and assessment process:(1) automatically generate context-free languages and grammars of different complexity; (2) pose different types of questions to the student with an appropriate response interface; (3) automatically correct the student answer, including grammar design for a given language; (4) provide feedback on errors. The application has been implemented as a plug-in of the SIETTE assessment system that in addition, can provide adaptive behaviour for question selection. It has been successfully used by more than a thousand students for formative and summative assessment.

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“…The online learning effect is positively correlated with the learners' emotional engagement in online learning [5][6][7][8]. Therefore, online learning platforms monitor learners' emotional engagement in real time to evaluate the teaching quality throughout the learning process [9][10][11][12][13][14][15]. In actual learning scenarios, learners have more and more personalized needs, the learning environment becomes increasingly complex, and the learning state is more difficult to detect.…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition of College Students’ Online Learning Engagement Based on Deep Learning

Wang¹

2022

Int. J. Emerg. Technol. Learn.

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In actual learning scenarios, learners have more and more personalized needs. The traditional measuring tools of emotional engagement can no longer meet the personalized needs of online learning. To solve the problem, this paper explores the emotion recognition of college students' online learning engagement based on deep learning. Firstly, the features were extracted from the texts related to online learning reviews and interactive behaviors of college students, and the texts were vectorized by the multi-head attention mechanism. Based on the multi-head attention mechanism, a bidirectional long short-term memory (BLSTM) emotion classification model was established, which describes the emotional attitude of learners towards learning engagement more clearly and more accurately. Through experiments, the proposed model was proved effective in emotion recognition of college students’ online learning engagement.

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Towards an Automatic Q&A Generation for Online Courses - A Pipeline Based Approach

Cited by 6 publications

References 6 publications

Triangle Generator for Online Mathematical E-learning

Triangle Generator for Online Mathematical E-learning

Teaching Compilers: Automatic Question Generation and Intelligent Assessment of Grammars' Parsing

Emotion Recognition of College Students’ Online Learning Engagement Based on Deep Learning

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