Investigating Learning Resources Precedence Relations via Concept Prerequisite Learning

Manrique, Rubén; Sosa, Juan Sebastián; Mariño, Olga; Nunes, Bernardo Pereira; Cardozo, Nicolás

doi:10.1109/wi.2018.00-89

Cited by 11 publications

(9 citation statements)

References 9 publications

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“…Although the results are slightly worse to those reported in Manrique et al (2018), a smaller number of features were employed here obtaining a precision greater than 90%. This supervised model is used as the core component in the prerequisite evaluation module (see Fig.…”

Section: Supervised Model Training Processcontrasting

confidence: 80%

“…Prerequisite relations exist as a natural dependency between concepts in cognitive processes when people learn, organize, apply, and generate knowledge (Laurence and Margolis 1999). Recently, there has been a growing interest in automatic approaches for identifying prerequisites (Liang et al 2015;Pan et al 2017;Wang et al 2016) and their applications in the organization of learning resources (Manrique et al 2018), and automatic reading list generation (Fabbri et al 2018). Most of these approaches take advantage of natural language processing techniques and machine learning strategies to extract latent connections among concepts in large document corpora to find prerequisite dependencies.…”

Section: Introductionmentioning

confidence: 99%

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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: Supervised Model Training Processcontrasting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Exploring knowledge graphs for the identification of concept prerequisites

Manrique

Pereira

Mariño

2019

Smart Learn. Environ.

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“…Chen et al [3] divided each lecture video into a length of 20-second-long segments and inferred precedence relations between the segments. Manrique et al [4] also identified precedence relations between MOOC videos with their main concepts. However, their main concepts were extracted by experts manually.…”

Section: Related Workmentioning

confidence: 99%

“…e prerequisite relation exists as a natural precedence among concepts in cognitive processes when people learn, organize, apply, and generate knowledge [1]. Concept prerequisite learning has played an important role in many fields, including precedence relation identification [2][3][4], concept graph extraction [5,6], reading list generation [7,8], curriculum planning [9], course sequencing [10], course design [11], knowledge tracing [12], and automatic assessment [13].…”

Section: Introductionmentioning

confidence: 99%

Mining Precedence Relations among Lecture Videos in MOOCs via Concept Prerequisite Learning

Xiao

Bai

Wang

2021

Mathematical Problems in Engineering

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In recent years, MOOC has gradually become an important way for people to learn knowledge. But the knowledge background of different people is quite different. Moreover, the precedence relations between lecture videos in a MOOC are often not clearly explained. As a result, some people may encounter obstacles due to lack of background knowledge when learning a MOOC. In this paper, we proposed an approach for mining precedence relations between lecture videos in a MOOC automatically. First, we extracted main concepts from video captions automatically. And then, an LSTM-based neural network model was used to measure prerequisite relations among the main concepts. Finally, the precedence relations between lecture videos were identified based on concept prerequisite relations. Experiments showed that our concept prerequisite learning method outperforms the existing methods and helps accurately identify the precedence relations between lecture videos in a MOOC.

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“…In recent years, the extracting of prerequisite relations among concepts has become a focus of researchers. Prerequisite relations among concepts have played a significant role in many applied fields of smart education, such as curriculum planning and design [1,2], student knowledge status tracking [3], concept map building [4,5], learner ranking [6,7,8], document reading list generation [9,10], and so on.…”

Section: Introductionmentioning

confidence: 99%

Extracting Prerequisite Relations among Concepts from the Introduction of Online Courses(SEKEEO) (S)

Wang¹,

Xiao²,

Qin³

et al. 2021

Proceedings of the 33rd International Conference on Software Engineering and Knowledge Engineering

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Affected by the COVID-19 pandemic, teaching tasks have gradually shifted from offline to online, which expanded online education resources unprecedentedly. "Concept" is a professional vocabulary in the curriculum. Exploring the prerequisite relations among concepts is of great significance to educational planning. This research extracts concepts from online course introduction and proposes a mixed method for extracting concept prerequisite relations. Experiments on public data set show that this method outperforms existing ones. Tests were also carried out on the datasets of eleven schools, which proves that this model has good scalability.

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Investigating Learning Resources Precedence Relations via Concept Prerequisite Learning

Cited by 11 publications

References 9 publications

Exploring knowledge graphs for the identification of concept prerequisites

Exploring knowledge graphs for the identification of concept prerequisites

Mining Precedence Relations among Lecture Videos in MOOCs via Concept Prerequisite Learning

Extracting Prerequisite Relations among Concepts from the Introduction of Online Courses(SEKEEO) (S)

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