ALEF: From Application to Platform for Adaptive Collaborative Learning

Bieliková, Mária; Šimko, Marián; Barla, Michal; Tvarožek, Jozef; Lábaj, Martin; Móro, Róbert; Srba, Ivan; Ševcech, Jakub

doi:10.1007/978-1-4939-0530-0_10

Cited by 24 publications

(18 citation statements)

References 21 publications

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“…Bieliková et al (2014) proposed the platform ALEF for adaptive collaborative learning. One of the functionalities of ALEF is to store and maintain information in the corresponding user and domain models, which can provide learners recommendations on how to achieve more successful collaboration.…”

Section: Collaborative Learning Improvementsmentioning

confidence: 99%

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A visual recommender tool in a collaborative learning experience

Anaya

Luque

Peinado

2016

Expert Systems with Applications

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Section: Collaborative Learning Improvementsmentioning

confidence: 99%

“…Experts quantified each dimension with an integral number ranging from -2 (very bad) to 2 (very good). Bieliková et al (2014) proposed, in their platform ALEF, two core models: 1) domain model, rich yet lightweight domain model semantically describes resources…”

Section: How To Model Collaborationmentioning

confidence: 99%

A visual recommender tool in a collaborative learning experience

Anaya

Luque

Peinado

2016

Expert Systems with Applications

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“…Among the few works that consider difficulty of questions or other learning objects, Bielikova et al [2] proposed an Adaptive Learning Framework that recommends sequence of items based on content similarity and knowledge prerequisites. Their field trial shows that using an adaptive system, where the learning object difficulty is an aspect of Figure 2: A translation to a question from the K9 system involving identifying triangles.…”

Section: Background and Related Workmentioning

confidence: 99%

Sequencing educational content in classrooms using Bayesian knowledge tracing

David

Segal

Gal

2016

Proceedings of the Sixth International Conference on Learning Analytics &Amp; Knowledge - LAK '16

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Despite the prevalence of e-learning systems in schools, most of today's systems do not personalize educational data to the individual needs of each student. This paper proposes a new algorithm for sequencing questions to students that is empirically shown to lead to better performance and engagement in real schools when compared to a baseline approach. It is based on using knowledge tracing to model students' skill acquisition over time, and to select questions that advance the student's learning within the range of the student's capabilities, as determined by the model. The algorithm is based on a Bayesian Knowledge Tracing (BKT) model that incorporates partial credit scores, reasoning about multiple attempts to solve problems, and integrating item difficulty. This model is shown to outperform other BKT models that do not reason about (or reason about some but not all) of these features. The model was incorporated into a sequencing algorithm and deployed in two classes in different schools where it was compared to a baseline sequencing algorithm that was designed by pedagogical experts. In both classes, students using the BKT sequencing approach solved more difficult questions and attributed higher performance than did students who used the expert-based approach. Students were also more engaged using the BKT approach, as determined by their interaction time and number of log-ins to the system, as well as their reported opinion. We expect our approach to inform the design of better methods for sequencing and personalizing educational content to students that will meet their individual learning needs.

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“…In the year of 2014, the authors "M. Bieliková, M. Šimko, M. Barla, J. Tvarožek, M. Labaj, R. Móro, I. Srba, and J. Ševcech" proposed a paper titled "ALEF: from Application to Platform for Adaptive Collaborative Learning" [2,10], in that they described such as: Web 2.0 has tremendously affected instruction. It encourages access and accessibility of learning content in assortment of new configurations, content creation, learning custom-made to understudies' individual inclinations, and coordinated effort.…”

Section: Literature Surveymentioning

confidence: 99%

“…Specifically, in educational domains the content management process is crucial because the number of students are increasing means the server spacing is also more and processing strategy will also be getting increased. So, that some intelligent process mining techniques are required to process the student performance analysis scheme [2]. This kind of processing are usually handled by means of classical data mining algorithms such as SVM, ANN and so on, these kinds of algorithms produces acceptable yields as well.…”

Section: Introductionmentioning

confidence: 99%

Semantic educational data extraction using structur-al domain relationships

Srikanth¹

2017

IJET

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In the mining industry', some of the domains are most popular and it plays a vital role in the specific area. Educational Mining and WebData Extraction are the two important factors play a leading role in mining industry. The main objective of the proposed system is to extract the related contents from web using semantic (relating to meaning in language or logic) principles as well as to allow the providers to dynamically generate the web pages for educational content and allow the users to search and extract the data from server based on content. The main model of this system is to illustrate the adaptive learning system. For demonstration we consider the semantic principles for Educational content over dynamic environment. This site allows the providers to create web pages related to educational content dynamically and this will be getting approved by the Administrator to live in process. Once the site is live the users can search for the exact content present into the site based on semantic principles. The proposed model is designed for dynamic web data extraction and content analysis from the extracted data due to educational principles. In the proposed system Semantic Web Extraction (SWE) procedures are highly analyzed and utilized for content manipulations. Energetic data extraction scheme for users based on educational content rather than header, title, meta tags and descriptions.

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ALEF: From Application to Platform for Adaptive Collaborative Learning

Cited by 24 publications

References 21 publications

A visual recommender tool in a collaborative learning experience

A visual recommender tool in a collaborative learning experience

Sequencing educational content in classrooms using Bayesian knowledge tracing

Semantic educational data extraction using structur-al domain relationships

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