Prerequisite Relation Learning for Concepts in MOOCs

Pan, Liangming; Li, Chengjiang; Li, Juanzi; Tang, Jie

doi:10.18653/v1/p17-1133

Cited by 106 publications

(76 citation statements)

References 19 publications

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“…To our knowledge, there have been three previous methods specifically designed to infer educational concept prerequisites, viz. CGL (Liu et al 2016), CPR-Recover (Liang et al 2017), and MOOC-RF (Pan et al 2017). CGL is a supervised learning approach to map courses from different universities onto a universal space of concepts and predict prerequisites between both courses and concepts (Liu et al 2016).…”

Section: Related Workmentioning

confidence: 99%

“…But the number of constraints in the optimization problem is proportional to the number of course prerequisite edges, which does not scale well with the size of the training data. Pan et al recently proposed a method MOOC-RF for concept prerequisite recovery from Coursera data (Pan et al 2017). They define various features and train a classifier that can identify prerequisite relations among concepts from video transcripts.…”

Section: Related Workmentioning

confidence: 99%

“…The method CPR-Recover (Liang et al 2017) is, to our knowledge, the best known method (though unsupervised) for inferring concept prerequisites as tested on the University Course Dataset, where they have shown that it outperforms previous methods including CGL (Liu et al 2016). MOOC-RF (Pan et al 2017) is a supervised method, designed for online video based courses. Note that they have a different problem setting and do not use course/video prerequisite pairs to infer concept prerequisites.…”

Section: Performance Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Inferring Concept Prerequisite Relations from Online Educational Resources

Roy

Madhyastha

Lawrence

et al. 2019

AAAI

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The Internet has rich and rapidly increasing sources of high quality educational content. Inferring prerequisite relations between educational concepts is required for modern large-scale online educational technology applications such as personalized recommendations and automatic curriculum creation. We present PREREQ, a new supervised learning method for inferring concept prerequisite relations. PRE-REQ is designed using latent representations of concepts obtained from the Pairwise Latent Dirichlet Allocation model, and a neural network based on the Siamese network architecture. PREREQ can learn unknown concept prerequisites from course prerequisites and labeled concept prerequisite data. It outperforms state-of-the-art approaches on benchmark datasets and can effectively learn from very less training data. PREREQ can also use unlabeled video playlists, a steadily growing source of training data, to learn concept prerequisites, thus obviating the need for manual annotation of course prerequisites.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Inferring Concept Prerequisite Relations from Online Educational Resources

Roy

Madhyastha

Lawrence

et al. 2019

AAAI

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“…Despite some works have investigated extracting key concepts in MOOCs [9,10,11], our work is different because the objective of our task is to jointly identify the position and type of resource mentions from plain texts. Therefore, it is more similar to Named Entity Recognition (NER), which seeks to locate named entities in texts and classify them into pre-defined categories.…”

Section: Named Entity Recognitionmentioning

confidence: 99%

Resource Mention Extraction for MOOC Discussion Forums

Pan

Kan

et al. 2019

IEEE Access

Self Cite

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In discussions hosted on discussion forums for Massive Online Open Courses (MOOCs), references to online learning resources are often of central importance. They contextualize the discussion, anchoring the discussion participants' presentation of the issues and their understanding. However they are usually mentioned in free text, without appropriate hyperlinking to their associated resource. Automated learning resource mention hyperlinking and categorization will facilitate discussion and searching within MOOC forums, and also benefit the contextualization of such resources across disparate views. We propose the novel problem of learning resource mention identification in MOOC forums; i.e., to identify resource mentions in discussions, and classify them into pre-defined resource types.As this is a novel task with no publicly available data, we first contribute a large-scale labeled dataset -dubbed the Forum Resource Mention (FoRM) dataset -to facilitate our current research and future research on this task. FoRM contains over 10, 000 real-world forum threads in collaboration with Coursera, with more than 23, 000 manually labeled resource mentions. We then formulate this task as a sequence tagging problem and investigate solution architectures to address the problem. Importantly, we identify $ * two major challenges that hinder the application of sequence tagging models to the task: (1) the diversity of resource mention expression, and (2) long-range contextual dependencies. We address these challenges by incorporating character-level and thread context information into a LSTM-CRF model. First, we incorporate a character encoder to address the out-ofvocabulary problem caused by the diversity of mention expressions. Second, to address the context dependency challenge, we encode thread contexts using an RNN-based context encoder, and apply the attention mechanism to selectively leverage useful context information during sequence tagging. Experiments on FoRM show that the proposed method improves the baseline deep sequence tagging models notably, significantly bettering performance on instances that exemplify the two challenges.

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“…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%

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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Prerequisite Relation Learning for Concepts in MOOCs

Cited by 106 publications

References 19 publications

Inferring Concept Prerequisite Relations from Online Educational Resources

Inferring Concept Prerequisite Relations from Online Educational Resources

Resource Mention Extraction for MOOC Discussion Forums

Exploring knowledge graphs for the identification of concept prerequisites

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