Graphical model: The means for simulation-based learning

Aukstakalnis, Nerijus; Baniulis, Kazys; Pauliute, Jurate; Slotkienė, Asta

doi:10.1109/iti.2008.4588456

Cited by 4 publications

References 6 publications

(2 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

Automated e-Assessment: Students’ Needs and e-Evaluation Solution Possibilities

Margienė¹,

Ramanauskaitė²

2022

IJIET

View full text Add to dashboard Cite

E-learning is increasingly gaining its usage. In the COVID-19 period, its importance even increased. One of the important e-learning processes is e-assessment. Suitable e-evaluation implementation depends on multiple factors. One of the factors for suitable testing implementation is alignment between users’ needs for a specific purpose testing and its implementation possibilities. Therefore in this paper, we analyze what are students’ needs for e-evaluation system in knowledge level testing and self-evaluation testing cases. Student survey and multi-criteria decision-making were used to find out how students rank different criteria of e-assessment and how linear, graph-based, and tree-based testing structures rank based on these criteria.

show abstract

Automated e-Assessment: Students’ Needs and e-Evaluation Solution Possibilities

Margienė¹,

Ramanauskaitė²

2022

IJIET

View full text Add to dashboard Cite

show abstract

Adaptyviųjų ir kompetencijomis grįstų žinių vertinimo sistemų duomenų struktūrų modeliai ir jų susiejimas

Margienė¹

View full text Add to dashboard Cite

Toward Adaptability of E-Evaluation: Transformation from Tree-Based to Graph-Based Structure

Margienė

Ramanauskaitė

2021

Applied Sciences

View full text Add to dashboard Cite

The COVID-19 pandemic and quarantine have forced students to use distance learning. Modern information technologies have enabled global e-learning usage but also revealed a lack of personalization and adaptation in the learning process when compared to face-to-face learning. While adaptive e-learning methods exist, their practical application is slow because of the additional time and resources needed to prepare learning material and its logical adaptation. To increase e-learning materials’ usability and decrease the design complexity of automated adaptive students’ work evaluation, we propose several transformations from a competence tree-based structure to a graph-based automated e-evaluation structure. Related works were summarized to highlight existing e-evaluation structures and the need for new transformations. Competence tree-based e-evaluation structure improvements were presented to support the implementation of top-to-bottom and bottom-to-top transformations. Validation of the proposed transformation was executed by analyzing different use-cases and comparing them to the existing graph-to-tree transformation. Research results revealed that the competence tree-based learning material storage is more reusable than graph-based solutions. Competence tree-based learning material can be transformed for different purposes in graph-based e-evaluation solutions. Meanwhile, graph-based learning material transformation to tree-based structure implies material redundancy, and the competence of the tree structure cannot be restored.

show abstract

Graphical model: The means for simulation-based learning

Cited by 4 publications

References 6 publications

Automated e-Assessment: Students’ Needs and e-Evaluation Solution Possibilities

Automated e-Assessment: Students’ Needs and e-Evaluation Solution Possibilities

Adaptyviųjų ir kompetencijomis grįstų žinių vertinimo sistemų duomenų struktūrų modeliai ir jų susiejimas

Toward Adaptability of E-Evaluation: Transformation from Tree-Based to Graph-Based Structure

Contact Info

Product

Resources

About