Prerequisites between learning objects: Automatic extraction based on a machine learning approach

Gasparetti, Fabio; Medio, Carlo De; Limongelli, Carla; Sciarrone, Filippo; Temperini, Marco

doi:10.1016/j.tele.2017.05.007

Cited by 52 publications

(30 citation statements)

References 23 publications

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“…Prerequisites are a way of making sure that learners enter into a new topic/concept with the prior knowledge required to its understanding. This, not only helps the learner to understand more easily, but it also helps he/she to feel more comfortable and confident with the subject matter (Gasparetti et al 2018). Although the lowest level of granularity for specifying prerequisites is between concepts, the prerequisites relationships can be naturally extended to define precedence relations between elements of greater granularity in instructional and non-instructional learning processes (e.g.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Authors of this work state that experiments conducted on a dataset composed by 150 HTML documents give a precision greater than 70% in the categorization of the concepts. Gasparetti et al (2018) uses a machine learning approach to identify a prerequisite relationship between two learning resources. This research assesses the relationship of prerequisites between learning resources and not between concepts.…”

Section: Background and Related Workmentioning

confidence: 99%

“…The automatic identification of prerequisite relationships between concepts has been identified as one of the cornerstones for modern, large-scale online educational applications (Gasparetti et al 2018;Talukdar and Cohen 2012;Pan et al 2017). Prerequisite relations exist as a natural dependency between concepts in cognitive processes when people learn, organize, apply, and generate knowledge (Laurence and Margolis 1999).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploring knowledge graphs for the identification of concept prerequisites

Manrique

Pereira

Mariño

2019

Smart Learn. Environ.

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Learning basic concepts before complex ones is a natural form of learning. Automated systems and instructional designers evaluate and order concepts' complexity to successfully generate and recommend or adapt learning paths. This paper addresses the specific challenge of accurately and adequately identifying concept prerequisites using semantic web technologies for a basic understanding of a particular concept within the context of learning: given a target concept c, the goals are to (a) find candidate concepts that serve as possible prerequisite for c; and, (b) evaluate the prerequisite relation between the target and candidates concepts via a supervised learning model. Our four step approach consists of (i) an exploration of Knowledge Graphs in order to identify possible candidate concepts; (ii) the creation of a set of potential concepts; (iii) deployment of supervised learning model to evaluate a proposed list of prerequisite relationships regarding the target set; and, (iv) validation of our approaching using a ground truth of 80 concepts from different domains (with a precision varying between 76% and 96%).

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Exploring knowledge graphs for the identification of concept prerequisites

Manrique

Pereira

Mariño

2019

Smart Learn. Environ.

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“…Other related works focus mainly on a very specific task. For example, [10] present a system that uses machine learning technics to detect if, for the understanding of a given resource, some previous requisites should be meet and if another resources can be used to learn that previous requisites. By doing so, they hope to fill the knowledge gap a new resource may arise for a student.…”

Section: Related Workmentioning

confidence: 99%

On the Identification of Several Key Issues on OER Discovery for Smart Learning Environments

Molins-Ruano

Jurado

Rodríguez

2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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The best Open Educational Resources (OER) for each final user can be hard to find. OER come from lots of sources, in many different formats, conforming to a diverse logical structure and each user may present different objectives depending on their role in the teaching/learning process and their context. Previous attempts have only focused on one kind of technologies. A new approach that embraces diversity, may gain from the potential synergies of sharing resources in the development of the final recommendation system and the exploitation of the data. In this work, we aim at identifying the main challenges facing the field of OER recommendation, for a potential architecture model.

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“…Along the paper, we use the terms Learning Object (LO) and Wikipedia page interchangeably: in a broad sense, Wikipedia entries can be considered Ls (Nash, 2005), moreover previous work in related fields represent educational units as Wikipedia pages (Gasparetti et al, 2018). This fits our needs since a Wikipedia page consists of textual content pertaining to a single unit of learning.…”

Section: Introductionmentioning

confidence: 99%

Linguistically-Driven Strategy for Concept Prerequisites Learning on Italian

Miaschi¹,

Alzetta²,

Cardillo³

et al. 2019

Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

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We present a new concept prerequisite learning method for Learning Object (LO) ordering that exploits only linguistic features extracted from textual educational resources. The method was tested in a cross-and indomain scenario both for Italian and English. Additionally, we performed experiments based on a incremental training strategy to study the impact of the training set size on the classifier performances. The paper also introduces ITA-PREREQ, to the best of our knowledge the first Italian dataset annotated with prerequisite relations between pairs of educational concepts, and describe the automatic strategy devised to build it.

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Prerequisites between learning objects: Automatic extraction based on a machine learning approach

Cited by 52 publications

References 23 publications

Exploring knowledge graphs for the identification of concept prerequisites

Exploring knowledge graphs for the identification of concept prerequisites

On the Identification of Several Key Issues on OER Discovery for Smart Learning Environments

Linguistically-Driven Strategy for Concept Prerequisites Learning on Italian

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